Podcasts by Data Science at Home

Data Science at Home

Technology, AI, machine learning and algorithms. Come join the discussion on Discord!
https://discord.gg/4UNKGf3

Further podcasts by Francesco Gadaleta

Podcast on the topic Technologie

All episodes

Data Science at Home
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246) from 2023-12-11T06:52

In this captivating podcast episode, join renowned financial expert Chris Skinner as he delves into the fascinating realm of the future of money. From cryptocurrencies to government currencies,...

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Data Science at Home
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245) from 2023-12-04T09:18:14

In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligenc...

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Data Science at Home
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244) from 2023-12-01T19:36:46

Brace yourselves, dear friends! In this episode, we delve into the earth-shattering revelation that OpenAI might have stumbled upon AGI (lol) and we're all just seconds away from being replaced...

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Data Science at Home
The AI Chip Chat 🤖💻(Ep. 243) from 2023-11-20T09:12:38

Dive into the cool world of AI chips with us! 🚀 We're breaking down how these special computer chips for AI have evolved and what makes them different. Think of them like the superheroes of the...

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Data Science at Home
Rolling the Dice: Engineering in an Uncertain World (Ep. 242) from 2023-11-09T17:40:38

Hey there, engineering enthusiasts! Ever wondered how engineers deal with the wild, unpredictable twists and turns in their projects? In this episode, we're spilling the beans on uncertainty and...

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Data Science at Home
How Language Models Are the Ultimate Database(Ep. 241) from 2023-10-22T08:48:02

In this episode, dive deep into the world of Language Models as we decode their intricate structure, revealing how these powerful algorithms exploit concepts from the past.

But... what if...

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Data Science at Home
Elon is right this time: Rust is the language of AI (Ep. 240) from 2023-10-09T09:14:13

In this episode, I delve into Elon Musk's foresight on the future of AI as he champions Rust programming language. Here is why Rust stands at the forefront of AI technology and the potential it...

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Data Science at Home
Attacking LLMs for fun and profit (Ep. 239) from 2023-09-18T05:11:25

As a continuation of Episode 238, I explain some effective and fun attacks to conduct against LLMs. Such attacks are even more effective on models served locally, that are hardly controlled by h...

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Data Science at Home
Unlocking Language Models: The Power of Prompt Engineering (Ep. 238) from 2023-09-11T08:00:14

Join me on an enlightening journey through the world of prompt engineering. Explore the multifaceted skills and strategies involved in harnessing the potential of large language models for vario...

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Data Science at Home
Erosion of Software Architecture Quality in the Age of AI Code Generation (Ep. 237) from 2023-08-30T08:51:58

In this era of AI-powered code generation, software architects are facing a concerning decline in the quality of their creations. The once meticulously crafted software architectures are now bei...

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Data Science at Home
The new dimension of AI: Vector Databases (Ep. 236) from 2023-08-17T13:06:05

Let's delve into the emerging trend in database design – or is it really a new trend? The realm of vector databases and their revolutionary influence on AI and ML is making headlines. Come alo...

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Data Science at Home
Building Self Serve Business Intelligence With AI and LLMs at Zenlytic (Ep. 235) from 2023-08-04T14:02:47

In this episode, we dive into the world of data analytics and artificial intelligence with Ryan, the CEO, and Paul, the CTO of Zenlytic. Having graduated from Harvard and with extensive backgrou...

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Data Science at Home
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner (Ep. 234) from 2023-07-09T21:37

In this captivating podcast episode, join renowned financial expert Chris Skinner as he delves into the fascinating realm of the future of money. From cryptocurrencies to government currencies,...

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Data Science at Home
Debunking AGI Hype and Embracing Reality (Ep. 233) from 2023-06-26T13:37:33

In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligenc...

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Data Science at Home
Full steam ahead! Unraveling Forward-Forward Neural Networks (Ep. 232) from 2023-06-15T20:34:22

In this exciting episode, we dive into the world of Forward-Forward Neural Networks, unveiling their mind-boggling power and potential. Join us as we demystify these advanced AI algorithms and ...

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Data Science at Home
The LLM Battle Begins: Google Bard vs ChatGPT (Ep. 231) from 2023-06-06T06:30

Brace yourselves as we uncover the mind-blowing AI model, Google Bard, that's poised to challenge ChatGPT and other conversational AI systems. Join us as we explore the revolutionary features of...

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Data Science at Home
Unleashing the Force: Blending Neural Networks and Physics for Epic Predictions (Ep. 230) from 2023-05-30T06:39:21

In this enlightening episode of our podcast, we delve into the fascinating realm of Physics Informed Neural Networks (PINNs) and explore how they combine the extraordinary prediction capabilitie...

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Data Science at Home
AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229) from 2023-05-25T06:46:41

In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles. With the advent of automation and intelligent systems, develop...

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Data Science at Home
Warning! Mathematical Mayhem Ahead: Demystifying Liquid Time-Constant Networks (Ep. 228) from 2023-05-16T06:17:57

Hold on to your calculators and buckle up for a wild mathematical ride in this episode! Brace yourself as we dive into the fascinating realm of Liquid Time-Constant Networks (LTCs), where mathem...

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Data Science at Home
Efficiently Retraining Language Models: How to Level Up Without Breaking the Bank (Ep. 227) from 2023-05-11T07:54:18

Get ready for an eye-opening episode! 🎙️

In our latest podcast episode, we dive deep into the world of LoRa (Low-Rank Adaptation) for large language models (LLMs). This groundbreaking te...

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Data Science at Home
Revolutionize Your AI Game: How Running Large Language Models Locally Gives You an Unfair Advantage Over Big Tech Giants (Ep. 226) from 2023-05-03T16:47:22

This is the first episode about the latest trend in artificial intelligence that's shaking up the industry - running large language models locally on your machine. This new approach allows you t...

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Data Science at Home
Rust: A Journey to High-Performance and Confidence in Code at Amethix Technologies (Ep. 225) from 2023-04-26T08:43:21

The journey of porting our projects to Rust was intense, but it was a decision we made to improve the quality of our software. The migration was not an easy task, as it required a considerable a...

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Data Science at Home
The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224) from 2023-04-18T14:15

In this episode of our podcast, we dive deep into the fascinating world of Graph Neural Networks.

First, we explore Hierarchical Networks, which allow for the efficient representation and...

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Data Science at Home
The Power of Graph Neural Networks: Understanding the Future of AI - Part 1/2 (Ep.223) from 2023-04-11T12:02:18

In this episode, I explore the cutting-edge technology of graph neural networks (GNNs) and how they are revolutionizing the field of artificial intelligence. I break down the complex concepts be...

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Data Science at Home
Leveling Up AI: Reinforcement Learning with Human Feedback (Ep. 222) from 2023-04-04T18:21:12

In this episode, we dive into the not-so-secret sauce of ChatGPT, and what makes it a different model than its predecessors in the field of NLP and Large Language Models.

We explore how h...

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Data Science at Home
The promise and pitfalls of GPT-4 (Ep. 221) from 2023-03-30T09:39:28

In this episode, we explore the potential of the highly anticipated GPT-4 language model and the challenges that come with its development. From its ability to generate highly coherent and creat...

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Data Science at Home
AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220) from 2023-03-14T18:22:58

In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles. With the advent of automation and intelligent systems, develop...

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Data Science at Home
Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218) from 2023-02-27T09:11

In this episode, we dive into the fascinating world of zero-knowledge proofs and their impact on data science. Zero-knowledge proofs allow one party to prove to another that they know a secret w...

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Data Science at Home
Deep learning vs tabular models (Ep. 217) from 2023-02-21T09:32:27

Deep learning methods are not as effective with tabular data. Here is why, and what to do about it.

 

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Data Science at Home
[RB] Online learning is better than batch, right? Wrong! (Ep. 216) from 2023-02-15T07:56:53

In this episode I speak about online learning systems and why blindly choosing such a paradigm can lead to very unpredictable and expensive outcomes. Also in this episode, I have to deal with an...

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Data Science at Home
Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215) from 2023-01-26T09:00:09

In this episode, I'll be discussing the capabilities and limitations of ChatGPT, an advanced language AI model. I'll go over its power to understand and respond to natural language, and its appl...

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Data Science at Home
Accelerating Perception Development with Synthetic Data (Ep. 214) from 2023-01-14T17:04:02

In this episode I am with Kevin McNamara, founder and CEO of Parallel Domain. We speak about a very effective method to generate synthetic data that is currently in production at Parallel Domain. E...

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Data Science at Home
Edge AI applications for military and space [RB] (Ep. 213) from 2022-12-13T18:50:48

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Data Science at Home
From image to 3D model (Ep. 212) from 2022-12-08T10:14:08

Is it possible to reconstruct a 3D model from a simple image?

Under certain constraints, it is! In this episode I tell you how.

 

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Data Science at Home
Machine learning is physics (Ep. 211) from 2022-12-02T19:37:03

What if we borrowed from physics some theories that would interpret deep learning and machine learning in general? Here is a list of plausible ways to interpret our beloved ML models and underst...

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Data Science at Home
Autonomous cars cannot drive. Here is why. (Ep. 210) from 2022-11-21T10:28:45

If you think that the problem of self-driving cars has been solved, think twice. As a matter of fact, the problem of self-driving cars cannot be solved with the technical solutions that companie...

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Data Science at Home
Evolution of data platforms (Ep. 209) from 2022-11-08T11:05:53

Let's look at the history of data platforms. How did they evolve? Why? Shall I switch to the latest architecture? Enjoy the show!

 

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Data Science at Home
[RB] Is studying AI in academia a waste of time? (Ep. 208) from 2022-11-02T09:52:45

Companies and other business entities are actively involved in defining data products and applied research every year. Academia has always played a role in creating new methods and solutions/alg...

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Data Science at Home
Private machine learning done right (Ep. 207) from 2022-10-25T10:08:51

There are many solutions to private machine learning. I am pretty confident when I say that the one we are speaking in this episode is probably one of the most feasible and reliable. I am with D...

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Data Science at Home
Edge AI for applications in military and space (Ep. 206) from 2022-10-15T17:01:11

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Data Science at Home
[RB] What are generalist agents and why they can change the AI game (Ep. 205) from 2022-10-05T08:56:10

That deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement. Generalist agents have great properties that can overcome some of the limit...

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Data Science at Home
LIDAR, cameras and autonomous vehicles (Ep. 204) from 2022-09-28T07:12:30

How does an autonomous vehicle see? How does it sense the road? They are equipped of many sensors, of course. Are they all powerful enough? Small enough to hide them and make your car look beau...

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Data Science at Home
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203) from 2022-09-20T19:54:04

Sometimes applications crash. Some other times applications crash because memory is exhausted. Such issues exist because of bugs in the code, or heavy memory usage for reasons that were not expe...

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Data Science at Home
Is studying AI in academia a waste of time? (Ep. 202) from 2022-09-13T07:03:50

Companies and other business entities are actively involved in defining data products and applied research every year. Academia has always played a role in creating new methods and solutions/alg...

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Data Science at Home
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201) from 2022-09-07T07:14:47

Neural networks are becoming massive monsters that are hard to train (without the "regular" 12 last-generation GPUs). Is there a way to skip that?

Let me introduce you to Zero-Cost proxie...

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Data Science at Home
Online learning is better than batch, right? Wrong! (Ep. 200) from 2022-06-13T06:40

In this episode I speak about online learning systems and why blindly choosing such a paradigm can lead to very unpredictable and expensive outcomes. Also in this episode, I have to deal with an...

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Data Science at Home
What are generalist agents and why they can change the AI game (Ep. 199) from 2022-06-03T13:34:42

That deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement. Generalist agents have great properties that can overcome some of the limit...

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Data Science at Home
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198) from 2022-05-27T10:04:59

In this episode, I am with Chip Kent, chief data scientist at Deephaven Data Labs.

We speak about streaming data, real-time, and other powerful tools p...

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Data Science at Home
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197) from 2022-05-16T18:04:46

In this episode I speak with Matt Swalley, Chief Business Officer of Omneky, an AI platform that generates, analyzes and optimizes personalized ad creatives...

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Data Science at Home
State of Artificial Intelligence 2022 (Ep. 196) from 2022-05-06T08:37:31

Let's take a break and think about the state of AI in 2022. In this episode I summarize the long report from the Stanford Institute for Human-Centered Artifi...

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Data Science at Home
Improving your AI by finding issues within data pockets (Ep. 195) from 2022-04-21T07:38:20

In this episode I have a conversation with, Itai Bar-Sinai, CPO & Cofounder of Mona.

We speak about several interesting points about data and monitoring. Why is AI monitoring so differen...

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Data Science at Home
Fake data that looks, feels, and behaves like production.(Ep.194) from 2022-04-13T17:51:01

I am with Ander Steele, data scientist and mathematician with a passion for privacy and Shannon Bayatpur, product manager with a background in technical writing and computer science, from Tonic...

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Data Science at Home
Batteries and AI in Automotive (Ep. 193) from 2022-04-01T07:27:08

In this episode my friend and I speak about AI, batteries and automotive. Dennis Berner, founder of Digitlabs has been operating in the field of automotive and batteries for a long time. His poi...

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Data Science at Home
Collect data at the edge [RB] (Ep. 192) from 2022-03-25T16:16:24

In this episode I speak with Manavalan Krishnan from Tsecond about capturing massive amounts of data at the edge with security and reliability in mind.

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Data Science at Home
Bayesian Machine Learning with Ravin Kumar (Ep. 191) from 2022-03-19T16:36:33

This is one episode where passion for math, statistics and computers are merged. I have a very interesting conversation with Ravin,  data scientist at Google where he uses data to inform decisi...

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Data Science at Home
What is spatial data science? With Matt Forest from Carto (Ep. 190) from 2022-03-02T12:10:28

In this episode I am with Matt Forrest, VP of Solutions Engineering at Carto. We speak about machine learning applied to spatial data, spatial SQL and GIS (Geographic Information System). Enjoy ...

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Data Science at Home
Connect. Collect. Normalize. Analyze. An interview with the people from Railz AI (Ep. 189) from 2022-02-22T13:35:34

In this episode I am with Pasha Zavari - Director of Data Science and Derek Manuge - Co-founder and CTO at Railz. Railz is a very interesting company with an incredible mission: normalizing and...

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Data Science at Home
History of data science [RB] (Ep. 188) from 2022-02-16T09:54:12

How did we get here? Who invented the methods data scientists use every day?

We answer such questions and much more in this wonderful e...

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Data Science at Home
Artificial Intelligence and Cloud Automation with Leon Kuperman from Cast.ai (Ep. 187) from 2022-02-08T12:54:24

In this episode I speak about AI and cloud automation with Leon Kuperman, co-founder and CTO at CAST AI. Formerly Vice President of Security Products OCI at Oracle, Leon’s professional experienc...

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Data Science at Home
Embedded Machine Learning: Part 5 - Machine Learning Compiler Optimization (Ep. 186) from 2022-02-03T09:59:21

This is the last episode of the series "Embedded ML" and I made it for the bravest :) I speak about machine learning compiler optimization to a much greater detail.

Enjoy the episode! Listen

Data Science at Home
Embedded Machine Learning: Part 4 - Machine Learning Compilers (Ep. 185) from 2022-01-25T07:51:48

In this episode I speak about machine learning compilers, the most important tools to bridge the gap between high level frontends, ML backends and hardware target architectures.

There are...

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Data Science at Home
Embedded Machine Learning: Part 3 - Network Quantization (Ep. 184) from 2022-01-20T09:42:44

In this episode I speak about neural network quantization, a technique that makes networks feasible for embedded systems and small devices.

There are many quantization techniques dependin...

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Data Science at Home
Embedded Machine Learning: Part 2 (Ep. 183) from 2022-01-15T08:51:03

In Part 2 of Embedded Machine Learning, I speak about one important technique to prune a neural network and perform inference on small devices. Such technique helps preserving most of the accura...

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Data Science at Home
Embedded Machine Learning: Part 1 (Ep.182) from 2022-01-10T06:15

This episode is the first of a series about Embedded Machine Learning. I explain the requirements of tiny devices and how it is possible to run machine learning models.<...

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Data Science at Home
History of Data Science (Ep. 181) from 2021-12-19T06:30

How did we get here? Who invented the methods data scientists use every day?

We answer such questions and much more in this wonderful e...

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Data Science at Home
Capturing Data at the Edge (Ep. 180) from 2021-12-14T08:20

In this episode I speak with Manavalan Krishnan from Tsecond about capturing massive amounts of data at the edge with security and reliability in mind.

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Data Science at Home
[RB] Composable Artificial Intelligence (Ep. 179) from 2021-12-07T08:25:30

If you think deep learning is a method to get to AGI, think again. Humans, as well as all mammals think in a... composable way.

Come chat with us on D...

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Data Science at Home
What is a data mesh and why it is relevant (Ep. 178) from 2021-11-30T07:40:08

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Data Science at Home
Environmentally friendly AI (Ep. 177) from 2021-11-23T10:15:36

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Data Science at Home
Do you fear of AI? Why? (Ep. 176) from 2021-11-16T06:27

This episode summarizes a study about trends of AI in 2021, the way AI is perceived by people of different background and some other weird questions.

For instance, would you have sexual i...

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Data Science at Home
Composable models and artificial general intelligence (Ep. 175) from 2021-11-09T13:55:35

If you think deep learning is a method to get to AGI, think again. Humans, as well as all mammals think in a... composable way.

 

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Data Science at Home
Ethics and explainability in AI with Erika Agostinelli from IBM (ep. 174) from 2021-11-02T07:46:44

AI, ethics, and explainability. Are these topics that only large corporations can spend resources on? Can product-focused startups even think about them?

We answer such questions in thi...

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Data Science at Home
Is neural hash by Apple violating our privacy? (Ep. 173) from 2021-10-26T15:43:26

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Data Science at Home
Fighting Climate Change as a Technologist (Ep. 172) from 2021-10-19T08:42:22

The content of this episode has been created by Sylvain Kerkour  Feel free to subscribe to his newsletter at https://kerkour.com<...

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Data Science at Home
AI in the Enterprise with IBM Global AI Strategist Mara Pometti (Ep. 171) from 2021-10-11T07:20

IBM Global AI Strategist Mara Pometti is IBM’s first AI Strategist. She defines and designs the strategy for AI solutions by revealing overlooked insights hidden in enterprises’ data. In this e...

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Data Science at Home
Speaking about data with Mikkel Settnes from Dreamdata.io (Ep. 170) from 2021-09-24T15:05

In this episode Mikkel and Francesco have a really interesting conversation about some key differences between large and small organization in approaching machine learning. Listen to the episod...

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Data Science at Home
Send compute to data with POSH data-aware shell (Ep. 169) from 2021-09-14T12:55:37

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Data Science at Home
How are organisations doing with data and AI? (Ep. 168) from 2021-09-07T15:15

A few weeks ago I was the guest of a very interesting show called "AI Today". In that episode I talked about some of the biggest trends emerging in AI and machine learning today as well as how ...

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Data Science at Home
Don't fight! Cooperate. Generative Teaching Networks (Ep. 167) from 2021-08-31T15:03

Remember GANs? Generative Adversarial Networks for synthetic data generation?  There is a new method called Generative Teaching Networks, that uses similar concepts - just quite the opposite :P ...

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Data Science at Home
CSV sucks. Here is why. (Ep. 166) from 2021-08-24T15:05

It's time we get serious about replacing the CSV format with something that, guess what? it has been around for so long.

In this episode I explain the good parts of CSV files and the not ...

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Data Science at Home
Reinforcement Learning is all you need. Or is it? (Ep. 165) from 2021-08-17T14:57:35

Is reinforcement learning sufficient to build truly intelligent machines? Listen to this episode to find out.

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Data Science at Home
What's happening with AI today? (Ep. 164) from 2021-08-11T13:20:45

In this episode I have a wonderful chat with Ronald Schmelzer and Kathleen Walch, authors of "AI Today" the top podcast for those wanting a no-hype, practical, real-world insight into what enterpri...

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Data Science at Home
2 effective ways to explain your predictions (Ep. 163) from 2021-08-03T09:52:11

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Data Science at Home
The Netflix challenge. Fair or what? (Ep. 162) from 2021-07-22T10:04:38

Remember the Netflix challenge?

It was a ton of money for the one who would have cracked the problem of recommending the best possible movie.

Was it a fair challenge? Did it work? ...

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Data Science at Home
Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161) from 2021-07-15T15:30

In this episode Fetch AI CTO Jonathan Ward speaks about decentralization, AI, blockchain for smart cities and the enterprise.
Below some great links about collective learning, smart contract...

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Data Science at Home
Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161) from 2021-07-15T15:30

In this episode Fetch AI CTO Jonathan Ward speaks about decentralization, AI, blockchain for smart cities and the enterprise. Below some great links about collective learning, smart contracts in...

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Data Science at Home
Apache Arrow, Ballista and Big Data in Rust with Andy Grove RB (Ep. 160) from 2021-07-08T15:04:30

Do you want to know the latest in big data analytics frameworks? Have you ever heard of Apache Arrow? Rust? Ballista? In this episode I speak with Andy G...

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Data Science at Home
GitHub Copilot: yay or nay? (Ep. 159) from 2021-07-06T09:27:05

It made already quite some noise in the news, GitHub copilot promises to be your pair programmer for life. In this episode I explain how and what GitHub copilot does. Should developers be happy,...

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Data Science at Home
Pandas vs Rust [RB] (Ep. 158) from 2021-07-01T07:24:55

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Data Science at Home
A simple trick for very unbalanced data (Ep. 157) from 2021-06-22T11:40:49

Data from the real world are never perfectly balanced. In this episode I explain a simple yet effective trick to train models with very unbalanced data. Enjoy the show!

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Data Science at Home
Time to take your data back with Tapmydata (Ep. 156) from 2021-06-15T07:02:05

In this episode I am with Gilbert Hill, head of strategy at https://tapmydata.com/ We speak about personal data, blockchain and the ability to control it and mo...

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Data Science at Home
True Machine Intelligence just like the human brain (Ep. 155) from 2021-06-04T08:18:57

In this episode I have a really interesting conversation with Karan Grewal, member of the research staff at Numenta where he investigates how biological princi...

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Data Science at Home
Delivering unstoppable data with Streamr (Ep. 154) from 2021-05-26T06:58:41

Delivering unstoppable data to unstoppable apps is now possible with Streamr Network

Streamr is a layer zero protocol for real-time data which powe...

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Data Science at Home
MLOps: the good, the bad and the ugly (Ep. 153) from 2021-05-24T15:20:58

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Data Science at Home
MLOps: what is and why it is important Part 2 (Ep. 152) from 2021-05-19T09:02:46

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Data Science at Home
MLOps: what is and why it is important (Ep. 151) from 2021-05-11T08:12

If you think that knowing Tensorflow and Scikit-learn is enough, think again. MLOps is one of those trendy terms today. What is MLOps and why is it important? In this episode I speak about th...

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Data Science at Home
Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150) from 2021-04-28T06:59:41

Your data is worth thousands a year. Why aren’t you getting your fair share? There is a company that has a mission: they want you to take back control and get paid for your data. In this episode I...

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Data Science at Home
Building high-growth data businesses with Lillian Pierson (Ep. 149) from 2021-04-19T08:29:57

In this episode I have an amazing conversation with Lillian Pierson from data-mania.com This is an action-packed episode on how data professionals can quick...

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Data Science at Home
Learning and training in AI times (Ep. 148) from 2021-04-13T10:00:43

Is there a gap between life sciences and data science? What's the situation when it comes to interdisciplinary research? In this episode I am with Laura Harris, Director of Training for the Inst...

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Data Science at Home
You are the product [RB] (Ep. 147) from 2021-04-11T20:26:52

In this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dange...

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Data Science at Home
Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146) from 2021-04-08T07:39:10

In this episode I speak with Ritchie Vink, the author of Polars, a crate that is the fastest dataframe library at date of speaking :) If you want to participate to an amazing Rust open source pr...

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Data Science at Home
Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145) from 2021-03-26T10:43:54

Do you want to know the latest in big data analytics frameworks? Have you ever heard of Apache Arrow? Rust? Ballista? In this episode I speak with Andy G...

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Data Science at Home
Pandas vs Rust (Ep. 144) from 2021-03-19T13:23:01

Pandas is the de-facto standard for data loading and manipulation. Python is the de-facto programming language for such operations. Rust is the underdog. Or is it? In this episode I am showing y...

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Data Science at Home
Concurrent is not parallel - Part 2 (Ep. 143) from 2021-03-13T08:46:54

In plain English, concurrent and parallel are synonyms. Not for a CPU. And definitely not for programmers. In this episode I summarize the ways to parallelize on different architectures and oper...

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Data Science at Home
Concurrent is not parallel - Part 1 (Ep. 142) from 2021-03-10T13:26:51

In plain English, concurrent and parallel are synonyms. Not for a CPU. And definitely not for programmers. In this episode I summarize the ways to parallelize on different architectures and oper...

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Data Science at Home
Backend technologies for machine learning in production (Ep. 141) from 2021-03-02T07:47:49

This is one of the most dynamic and fascinating topics: API technologies for machine learning.

It's always fun to build ML models. But how about serving them in the real world? In this ep...

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Data Science at Home
You are the product (Ep. 140) from 2021-02-22T17:16:17

In this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dange...

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Data Science at Home
How to reinvent banking and finance with data and technology (Ep. 139) from 2021-02-15T09:41

The financial system is changing. It is becoming more efficient and integrated with many more services making our life more... digital. Is the old banking system doomed to fail? Or will it just...

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Data Science at Home
What's up with WhatsApp? (Ep. 138) from 2021-02-07T09:30:06

Have you clicked the button? Accepted the new terms?

It's time we have a talk.

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Is Rust flexible enough for a flexible data model? (Ep. 137) from 2021-02-01T08:54:33

In this podcast I get inspired by Paul Done's presentation about The Six Principles for Building Robust Yet Flexible Shared Data Appl...

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Data Science at Home
Is Apple M1 good for machine learning? (Ep.136) from 2021-01-25T18:10:42

In this episode I explain the basics of computer architecture and introduce some features of the Apple M1

Is it good for Machine Learning tasks?

 

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Data Science at Home
Rust and deep learning with Daniel McKenna (Ep. 135) from 2021-01-18T09:25:05

In this episode I speak with Daniel McKenna about Rust, machine learning and artificial intelligence.

You can find Daniel from

Data Science at Home
Scaling machine learning with clusters and GPUs (Ep. 134) from 2020-12-31T15:21:45

Let's finish this year with an amazing episode about scaling ML with clusters and GPUs. Kind of as a continuation of Amethix use advanced Artificial Intellig...

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Data Science at Home
A Standard for the Python Array API (Ep. 132) from 2020-12-08T09:10:01

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Distill data and train faster, better, cheaper (Ep. 128) from 2020-11-17T10:12:12

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Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127) from 2020-11-11T08:27:33

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Data Science at Home
Top-3 ways to put machine learning models into production (Ep. 126) from 2020-11-07T10:26:14

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Remove noise from data with deep learning (Ep.125) from 2020-11-03T08:25:45

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What is contrastive learning and why it is so powerful? (Ep. 124) from 2020-10-30T18:48:03

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Data Science at Home
Neural search (Ep. 123) from 2020-10-23T06:06

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Let's talk about federated learning (Ep. 122) from 2020-10-18T14:20:11

Let's talk about federated learning. Why is it important? Why large organizations are not ready yet?

 

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How to test machine learning in production (Ep. 121) from 2020-10-11T10:52:58

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Why synthetic data cannot boost machine learning (Ep. 120) from 2020-09-26T13:57:08

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Machine learning in production: best practices [LIVE from twitch.tv] (Ep. 119) from 2020-09-16T12:32:39

Hey there! Having the best time of my life ;)

This is the first episode I record while I am live on my new Twitch channel :) So much fun! Listen

Data Science at Home
Testing in machine learning: checking deeplearning models (Ep. 118) from 2020-09-04T13:11:32

In this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning. We cover testing with de...

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Data Science at Home
Testing in machine learning: generating tests and data (Ep. 117) from 2020-08-29T16:57:57

In this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

 

On Septe...

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Data Science at Home
Why you care about homomorphic encryption (Ep. 116) from 2020-08-12T07:13:12

After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype. This time it's all about pr...

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Data Science at Home
Test-First machine learning (Ep. 115) from 2020-08-03T16:16:40

In this episode I speak about a testing methodology for machine learning models that are supposed to be integrated in production environments.

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GPT-3 cannot code (and never will) (Ep. 114) from 2020-07-26T12:55:50

The hype around GPT-3 is alarming and gives and provides us with the awful picture of people misunderstanding artificial intelligence. In response to some comments that claim GPT-3 will take dev...

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Data Science at Home
Make Stochastic Gradient Descent Fast Again (Ep. 113) from 2020-07-22T10:53:18

There is definitely room for improvement in the family of algorithms of stochastic gradient descent. In this episode I explain a relatively simple method that has shown to improve on the Adam op...

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Data Science at Home
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112) from 2020-07-19T12:30:56

In this episode I speak about data transformation frameworks available for the data scientist who writes Python code. The usual suspect is clearly Pandas, as the most widely used library and de...

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[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111) from 2020-07-03T14:18:27

In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in...

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Rust and machine learning #4: practical tools (Ep. 110) from 2020-06-29T10:30

In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community ef...

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Rust and machine learning #3 with Alec Mocatta (Ep. 109) from 2020-06-22T11:48:40

In the 3rd episode of Rust and machine learning I speak with Alec Mocatta. Alec is a +20 year experience professional programmer who has been spending time at the interception of distributed sy...

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Data Science at Home
Rust and machine learning #2 with Luca Palmieri (Ep. 108) from 2020-06-19T11:17:40

In the second episode of Rust and Machine learning I am speaking with Luca Palmieri, who has been spending a large part of his career at the interception of machine learning and data engineering...

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Data Science at Home
Rust and machine learning #1 (Ep. 107) from 2020-06-17T07:14:43

This is the first episode of a series about the Rust programming language and the role it can play in the machine learning field.

Rust is one of t...

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Data Science at Home
Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106) from 2020-06-15T07:29

In this episode I have a chat with Sandeep Pandya, CEO at Everguard.ai a company that uses sensor fusion, computer vision and more to provide safer working en...

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Data Science at Home
Compressing deep learning models: rewinding (Ep.105) from 2020-06-01T08:37:33

As a continuation of the previous episode in this one I cover the topic about compressing deep l...

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Data Science at Home
Compressing deep learning models: distillation (Ep.104) from 2020-05-20T08:04:10

Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy du...

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Data Science at Home
Pandemics and the risks of collecting data (Ep. 103) from 2020-05-08T09:59:11

Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over.

 

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Attacks to machine learning model: inferring ownership of training data (Ep. 99) from 2020-03-14T10:15:06

In this episode I explain a very effective technique that allows one to infer the membership of any record at hand to the (private) training dataset used to train the target model. The effective...

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Data Science at Home
Don't be naive with data anonymization (Ep. 98) from 2020-03-08T16:33:31

Masking, obfuscating, stripping, shuffling.  All the above techniques try to do one simple thing: keeping the data private while sharing it with third parties. Unfortunately, they are not the si...

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Data Science at Home
Why sharing real data is dangerous (Ep. 97) from 2020-03-01T09:20:38

There are very good reasons why a financial institution should never share their data. Actually, they should never even move their data. Ever. In this episode I explain you why.

 

...

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Data Science at Home
Building reproducible machine learning in production (Ep. 96) from 2020-02-22T16:59:54

Building reproducible models is essential for all those scenarios in which the lead developer is collaborating with other team members. Reproducibility in machine learning shall not be an art, r...

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Data Science at Home
Bridging the gap between data science and data engineering: metrics (Ep. 95) from 2020-02-14T17:20:44

Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created ...

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Data Science at Home
A big welcome to Pryml: faster machine learning applications to production (Ep. 94) from 2020-02-07T10:20:54

Why so much silence? Building a company! That's why :)  I am building pryml, a platform that allows data scientists build their applications on data they cannot ge...

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Data Science at Home
It's cold outside. Let's speak about AI winter (Ep. 93) from 2019-12-31T13:15:09

In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine...

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Data Science at Home
The dark side of AI: bias in the machine (Ep. 92) from 2019-12-28T08:12:06

 

This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the m...

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The dark side of AI: metadata and the death of privacy (Ep. 91) from 2019-12-23T15:30:01

Get in touch with us Join the discussion about data science, machine learning and artificial intelligence on our Discord server   Episode transcrip...

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Data Science at Home
The dark side of AI: recommend and manipulate (Ep. 90) from 2019-12-11T11:07:38

In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the diffe...

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Data Science at Home
The dark side of AI: social media and the optimization of addiction (Ep. 89) from 2019-12-03T03:00

Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this: “I feel tremendous guilt. The short-term, dopamine-driven fe...

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Data Science at Home
More powerful deep learning with transformers (Ep. 84) (Rebroadcast) from 2019-11-27T23:24:40

Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept alre...

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Data Science at Home
How to improve the stability of training a GAN (Ep. 88) from 2019-11-18T03:30

Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of...

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Data Science at Home
What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87) from 2019-11-12T07:21:53

What happens to a neural network trained with random data?

Are massive neural networks just lookup tables or do they truly learn something? 

Today’s episode will be about memorisa...

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Data Science at Home
Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86) from 2019-11-05T08:33:21

In this episode I speak with Jon Krohn, author of Deeplearning Illustrated a book that makes deep learning easier to grasp.  We also talk abou...

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Data Science at Home
[RB] How to generate very large images with GANs (Ep. 85) from 2019-11-04T08:41:32

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In this episode I explain how a research group from the University of Lubeck do...

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Data Science at Home
More powerful deep learning with transformers (Ep. 84) from 2019-10-27T07:59:59

Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept alre...

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Data Science at Home
[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83) from 2019-10-18T05:13

Join the discussion on our Discord server

 

In this episode, I am with Discord server

 

After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing f...

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Data Science at Home
Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81) from 2019-10-10T09:23:02

Join the discussion on our Discord server

 

In this episode I have an amazing conversation with Jimmy Soni and Rob Goodman, authors of “A m...

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Data Science at Home
Attacking machine learning for fun and profit (with the authors of SecML Ep. 80) from 2019-10-01T02:51

Join the discussion on our Discord server

As ML plays a more and more relevant role in many domains of everyday life, it’s quite obvious to see mo...

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Data Science at Home
[RB] How to scale AI in your organisation (Ep. 79) from 2019-09-26T06:06

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Scaling technology and business processes are not equal. Since the beginning of...

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Data Science at Home
Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78) from 2019-09-23T05:40:59

Join the discussion on our Discord server

In this episode, I am with Discord server

Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain a...

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Data Science at Home
How to generate very large images with GANs (Ep. 76) from 2019-09-06T14:45:05

Join the discussion on our Discord server

In this episode I explain how a research group from the University of Lubeck do...

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Data Science at Home
[RB] Complex video analysis made easy with Videoflow (Ep. 75) from 2019-08-29T08:05:43

In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applicat...

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Data Science at Home
[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74) from 2019-08-27T02:01:32

In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep...

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Data Science at Home
How to cluster tabular data with Markov Clustering (Ep. 73) from 2019-08-21T00:40:38

In this episode I explain how a community detection algorithm known as Markov clustering can be constructed by combining simple concepts like random walks, graphs, similarity matrix. Moreover, I...

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Data Science at Home
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72) from 2019-08-14T05:16:06

The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and the Agile Methodology. In this episode I make a comp...

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Data Science at Home
Training neural networks faster without GPU (Ep. 71) from 2019-08-06T06:37:53

Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural n...

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Data Science at Home
Validate neural networks without data with Dr. Charles Martin (Ep. 70) from 2019-07-23T06:38:09

In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep...

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Data Science at Home
Complex video analysis made easy with Videoflow (Ep. 69) from 2019-07-16T02:00

In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applicat...

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Data Science at Home
Episode 68: AI and the future of banking with Chris Skinner [RB] from 2019-07-09T16:37:47

In this episode I have a wonderful conversation with Chris Skinner.

Chris and I recently got in touch at The banking scene 2019, fintech conference recently held ...

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Episode 67: Classic Computer Science Problems in Python from 2019-07-02T09:07:05

Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.

His book deepens your knowledge of problem solving techniques from t...

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Episode 66: More intelligent machines with self-supervised learning from 2019-06-25T07:12

In this episode I talk about a new paradigm of learning, which can be found a bit blurry and not really different from the other methods we know of, such as supervised and unsupervised learning....

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Episode 65: AI knows biology. Or does it? from 2019-06-23T13:18:50

The successes of deep learning for text analytics, also introduced in a recent post about sentiment analysis and published here are...

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Episode 64: Get the best shot at NLP sentiment analysis from 2019-06-14T14:21:22

The rapid diffusion of social media like Facebook and Twitter, and the massive use of different types of forums like Reddit, Quora, etc., is producing an impressive amount of text data every day...

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Episode 63: Financial time series and machine learning from 2019-06-04T12:35

In this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnog Alexandr has written very interesting ...

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Episode 62: AI and the future of banking with Chris Skinner from 2019-05-28T08:50:01

In this episode I have a wonderful conversation with Chris Skinner.

Chris and I recently got in touch at The banking scene 2019, fintech conference recently held ...

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Episode 61: The 4 best use cases of entropy in machine learning from 2019-05-21T12:20:48

It all starts from physics. The entropy of an isolated system never decreases… Everyone at school, at some point of his life, learned this in his physics class. What does this have to do with ma...

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Episode 60: Predicting your mouse click (and a crash course in deeplearning) from 2019-05-16T07:25:55

Deep learning is the future. Get a crash course on deep learning. Now! In this episode I speak to Oliver Zeigermann, author of Deep Learning Crash Course published by M...

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Episode 59: How to fool a smart camera with deep learning from 2019-05-07T14:07:29

In this episode I met three crazy researchers from KULeuven (Belgium) who found a method to fool surveillance cameras and stay hidden just by holding a special t-shirt.  We...

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Episode 58: There is physics in deep learning! from 2019-04-30T09:30:34

There is a connection between gradient descent based optimizers and the dynamics of damped harmonic oscillators. What does that mean? We now have a better theory for opt...

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Episode 57: Neural networks with infinite layers from 2019-04-23T10:04:27

How are differential equations related to neural networks? What are the benefits of re-thinking neural network as a differential equation engine? In this episode we expl...

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Episode 56: The graph network from 2019-04-16T08:32

Since the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathema...

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Episode 55: Beyond deep learning from 2019-04-09T08:47

The successes that deep learning systems have achieved in the last decade in all kinds of domains are unquestionable. Self-driving cars, skin cancer diagnostics, movie a...

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Episode 54: Reproducible machine learning from 2019-03-09T14:53:29

In this episode I speak about how important reproducible machine learning pipelines are. When you are collaborating with diverse teams, several tasks will be distributed among different individ...

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Episode 53: Estimating uncertainty with neural networks from 2019-01-23T09:37:49

Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by desig...

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Episode 52: why do machine learning models fail? [RB] from 2019-01-17T07:28:33

The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specif...

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Episode 51: Decentralized machine learning in the data marketplace (part 2) from 2019-01-08T12:13:55

In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Listen

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Episode 50: Decentralized machine learning in the data marketplace from 2018-12-26T17:06:52

In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, th...

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Episode 49: The promises of Artificial Intelligence from 2018-12-19T20:56:17

It's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most mislea...

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Episode 48: Coffee, Machine Learning and Blockchain from 2018-10-21T22:42:45

In this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home of...

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Episode 47: Are you ready for AI winter? [Rebroadcast] from 2018-09-11T10:44:27

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in th...

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Episode 46: why do machine learning models fail? (Part 2) from 2018-09-04T11:07:53

In this episode I continue the conversation from the previous one, about failing machine learning models.

When data scientists have access to the distributions of training and testing dat...

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Episode 45: why do machine learning models fail? from 2018-08-28T10:02:30

The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a ...

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Episode 44: The predictive power of metadata from 2018-08-21T08:01:22

In this episode I don't talk about data. In fact, I talk about metadata.

While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been ...

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Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro) from 2018-08-14T08:22:52

Today’s episode is about text analysis with python. Python is the de facto standard in machine learning. A large community, a generous choice in the set of libraries, at the price of less perfo...

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Episode 42: Attacking deep learning models (rebroadcast) from 2018-08-07T07:21:55

Attacking deep learning models Compromising AI for fun and profit

 

Deep learning models have shown very promising results in computer vision and sound ...

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Episode 41: How can deep neural networks reason from 2018-07-31T07:16:45

Today’s episode  will be about deep learning and reasoning. There has been a lot of discussion about the effectiveness of deep learning models and their capability to ge...

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Episode 40: Deep learning and image compression from 2018-07-24T07:15:16

Today’s episode  will be about deep learning and compression of data, and in particular compressing images. We all know how important compressing data is, reducing the size of digital objects wi...

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Episode 39: What is L1-norm and L2-norm? from 2018-07-19T11:44:47

In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.

 

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Episode 38: Collective intelligence (Part 2) from 2018-07-17T09:45:19

In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence. I am moving the conversation towards th...

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Episode 38: Collective intelligence (Part 1) from 2018-07-12T08:59:55

This is the first part of the amazing episode with Johannes Castner, CEO and founder of CollectiWise. Johannes is finishing his PhD in Sustainable Development from Colum...

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Episode 37: Predicting the weather with deep learning from 2018-07-09T08:35:50

Predicting the weather is one of the most challenging tasks in machine learning due to the fact that physical phenomena are dynamic and riche of events. Moreover, most of traditional approaches ...

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Episode 36: The dangers of machine learning and medicine from 2018-07-03T13:44:15

Humans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s w...

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Data Science at Home
Episode 35: Attacking deep learning models from 2018-06-29T11:44:13

Attacking deep learning models Compromising AI for fun and profit

 

Deep learning models have shown very promising results in computer vision and sound ...

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Data Science at Home
Episode 34: Get ready for AI winter from 2018-06-22T11:05:42

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in th...

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Episode 33: Decentralized Machine Learning and the proof-of-train from 2018-06-11T12:21

In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model...

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Episode 32: I am back. I have been building fitchain from 2018-06-04T12:45

I know, I have been away too long without publishing much in the last 3 months. But, there's a reason for that. I have been building a platform that combines machine le...

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Data Science at Home
Founder Interview – Francesco Gadaleta of Fitchain from 2018-05-24T12:51:27

Cross-posting from Cryptoradio.io Overview

Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that...

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Data Science at Home
Episode 31: The End of Privacy from 2018-04-02T12:37:58

Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using ...

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Data Science at Home
Episode 30: Neural networks and genetic evolution: an unfeasible approach from 2017-11-21T13:15:33

Despite what researchers claim about genetic evolution, in this episode we give a realistic view of the field.

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Data Science at Home
Episode 29: Fail your AI company in 9 steps from 2017-11-11T18:44:18

In order to succeed with artificial intelligence, it is better to know how to fail first. It is easier than you think. Here are 9 easy steps to fail your AI startup.

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Data Science at Home
Episode 28: Towards Artificial General Intelligence: preliminary talk from 2017-11-04T18:04:25

The enthusiasm for artificial intelligence is raising some concerns especially with respect to some ventured conclusions about what AI can really do and what its direct ...

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Data Science at Home
Episode 26: Deep Learning and Alzheimer from 2017-10-23T12:35:58

In this episode I speak about Deep Learning technology applied to Alzheimer disorder prediction. I had a great chat with Saman Sarraf, machine learning engineer at Konica Minolta, former lab man...

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Data Science at Home
Episode 25: How to become data scientist [RB] from 2017-10-16T14:45:16

In this episode, I speak about the requirements and the skills to become data scientist and join an amazing community that is changing the world with data analyticsa

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Data Science at Home
Episode 24: How to handle imbalanced datasets from 2017-10-09T02:45:59

In machine learning and data science in general it is very common to deal at some point with imbalanced datasets and class distributions. This is the typical case where ...

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Data Science at Home
Episode 23: Why do ensemble methods work? from 2017-10-03T13:05

Ensemble methods have been designed to improve the performance of the single model, when the single model is not very accurate. According to the general definition of en...

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Data Science at Home
Episode 22: Parallelising and distributing Deep Learning from 2017-09-25T17:38:36

Continuing the discussion of the last two episodes, there is one more aspect of deep learning that I would love to consider and therefore left as a full episode, that is...

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Data Science at Home
Episode 21: Additional optimisation strategies for deep learning from 2017-09-18T14:23:16

In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to ...

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Data Science at Home
Episode 20: How to master optimisation in deep learning from 2017-08-28T17:06:32

The secret behind deep learning is not really a secret. It is function optimisation. What a neural network essentially does, is optimising a function. In this episode I ...

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Data Science at Home
Episode 19: How to completely change your data analytics strategy with deep learning from 2017-08-09T17:39:25

Over the past few years, neural networks have re-emerged as powerful machine-learning models, reaching state-of-the-art results in several fields like image recognition ...

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Data Science at Home
Episode 18: Machines that learn like humans from 2017-03-28T13:00

Artificial Intelligence allow machines to learn patterns from data. The way humans learn however is different and more efficient. With Lifelong Machine Learning, machine...

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Data Science at Home
Episode 17: Protecting privacy and confidentiality in data and communications from 2017-02-15T13:00

Talking about security of communication and privacy is never enough, especially when political instabilities are driving leaders towards decisions that will affect people on a global scale

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Data Science at Home
Episode 16: 2017 Predictions in Data Science from 2016-12-23T13:00

We strongly believe 2017 will be a very interesting year for data science and artificial intelligence. Let me tell you what I expect and why.

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Data Science at Home
Episode 15: Statistical analysis of phenomena that smell like chaos from 2016-12-05T13:00

Is the market really predictable? How do stock prices increase? What is their dynamics? Here is what I think about the magics and the reality of predictions applied to markets and the stock exch...

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Data Science at Home
Episode 14: The minimum required by a data scientist from 2016-09-27T13:00

Why the job of the data scientist can disappear soon. What is required by a data scientist to survive inflation.

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Data Science at Home
Episode 13: Data Science and Fraud Detection at iZettle from 2016-09-06T13:00

Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection com...

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Data Science at Home
Episode 12: EU Regulations and the rise of Data Hijackers from 2016-07-26T13:00

Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the informa...

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Data Science at Home
Episode 11: Representative Subsets For Big Data Learning from 2016-05-03T13:00

How would you perform accurate classification on a very large dataset by just looking at a sample of it

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Data Science at Home
Episode 10: History and applications of Deep Learning from 2016-03-14T12:00

What is deep learning?If you have no patience, deep learning is the result of training many layers of non-linear processing units for feature extraction and data transformation e.g. from pixel, ...

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Data Science at Home
Episode 9: Markov Chain Montecarlo with full conditionals from 2016-03-02T13:00

At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution.

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Data Science at Home
Episode 8: Frequentists and Bayesians from 2016-02-15T13:00

There are statisticians and data scientists... Among statisticians, there are some who just count. Some others who… think differently. In this show we explore the old time dilemma between freque...

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Data Science at Home
Episode 7: 30 min with data scientist Sebastian Raschka from 2016-02-15T13:00

In this show I interview Sebastian Raschka, data scientist and author of Python Machine Learning.In addition to the fun we had offline, there are great elements about machine learning, data scie...

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Data Science at Home
Episode 6: How to be data scientist from 2016-01-19T13:00

In this episode, we tell you how to become data scientist and join an amazing community that is changing the world with data analytics.

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Data Science at Home
Episode 5: Development and Testing Practices in Data Science from 2016-01-13T13:00

Should data scientists follow the old good practices of software engineering? Data scientists make software after all.

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Data Science at Home
Episode 1: Predictions in Data Science for 2016 from 2015-12-21T13:00

It’s time to experiment with Data Science at home. Since we are still dealing with our hosting service, consider the first episode purely experimental, even though the content might be of your i...

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Data Science at Home
Episode 4: BigData on your desk from 2015-07-23T13:00

Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk.

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Data Science at Home
Episode 2: Networks and Graph Databases from 2015-07-23T13:00

Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk.

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Data Science at Home
Episode 3: Data Science and Bio-Inspired Algorithms from 2015-07-08T13:00

In this episode I meet Dr Eliseo Ferrante, formerly at the University of Leuven, currently researcher at the Université de Technologie de Compiègne, who studies self-organization and evolution.

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