Data Science at Home

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 the most beautiful languages I have ever studied so far. I personally come from the C programming language, though for professional activities in machine learning I had to switch to the loved and hated Python language. This episode is clearly not providing you with an exhaustive list of the benefits of Rust, nor its capabilities. For this you can check the references and start getting familiar with what I think it's going to be the language of the next 20 years. Sponsored This episode is supported by Pryml Technologies. Pryml offers secure and cost effective data privacy solutions for your organisation. It generates a synthetic alternative without disclosing you confidential data. References The Rust Programming Language Cookin' with Rust

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 environments to workers in heavy industry. Sandeep is a senior executive who can hide the complexity of the topic with great talent. This episode is supported by Pryml.io Pryml is an enterprise-scale platform to synthesise data and deploy applications built on that data back to a production environment. Test ideas. Launch new products. Fast. Secure.

As a continuation of the previous episode in this one I cover the topic about compressing deep learning models and explain another simple yet fantastic approach that can lead to much smaller models that still perform as good as the original one. Don't forget to join our Slack channel and discuss previous episodes or propose new ones. This episode is supported by Pryml.io Pryml is an enterprise-scale platform to synthesise data and deploy applications built on that data back to a production environment. References Comparing Rewinding and Fine-tuning in Neural Network Pruning https://arxiv.org/abs/2003.02389

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 during inference. In this episode I explain one of the first methods: knowledge distillation Come join us on Slack Reference Distilling the Knowledge in a Neural Network https://arxiv.org/abs/1503.02531 Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks https://arxiv.org/abs/2004.05937

Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over. Join our new Slack channel This episode is supported by Proton. You can check them out at protonmail.com or protonvpn.com

Whenever people reason about probability of events, they have the tendency to consider average values between two extremes. In this episode I explain why such a way of approximating is wrong and dangerous, with a numerical example. We are moving our community to Slack. See you there!

In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). While there are several flavors of ReLU in the literature, in this episode I speak about a very interesting approach that keeps computational complexity low while improving performance quite consistently. This episode is supported by pryml.io. At pryml we let companies share confidential data. Visit our website. Don't forget to join us on discord channel to propose new episode or discuss the previous ones. References Dynamic ReLU https://arxiv.org/abs/2003.10027

One of the best features of neural networks and machine learning models is to memorize patterns from training data and apply those to unseen observations. That's where the magic is. However, there are scenarios in which the same machine learning models learn patterns so well such that they can disclose some of the data they have been trained on. This phenomenon goes under the name of unintended memorization and it is extremely dangerous. Think about a language generator that discloses the passwords, the credit card numbers and the social security numbers of the records it has been trained on. Or more generally, think about a synthetic data generator that can disclose the training data it is trying to protect. In this episode I explain why unintended memorization is a real problem in machine learning. Except for differentially private training there is no other way to mitigate such a problem in realistic conditions. At Pryml we are very aware of this. Which is why we have been developing a synthetic data generation technology that is not affected by such an issue. This episode is supported by Harmonizely. Harmonizely lets you build your own unique scheduling page based on your availability so you can start scheduling meetings in just a couple minutes. Get started by connecting your online calendar and configuring your meeting preferences. Then, start sharing your scheduling page with your invitees! References The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks https://www.usenix.org/conference/usenixsecurity19/presentation/carlini

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 effectiveness of such technique is due to the fact that it works on black-box models of which there is no access to the data used for training, nor model parameters and hyperparameters. Such a scenario is very realistic and typical of machine learning as a service APIs. This episode is supported by pryml.io, a platform I am personally working on that enables data sharing without giving up confidentiality. As promised below is the schema of the attack explained in the episode. References Membership Inference Attacks Against Machine Learning Models

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 silver bullet to confidentiality. All the players in the synthetic data space rely on simplistic techniques that are not secure, might not be compliant and risky for production. At pryml we do things differently.

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.

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, rather it should be achieved via a methodical approach. In this episode I give a few suggestions about how to make your ML models reproducible and keep your workflow as smooth. Enjoy the show! Come visit us on our discord channel and have a chat

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 by data scientists don't find a match in production, just because they are not production-ready. In this episode I have a talk with Daan Gerits, co-founder and CTO at Pryml.io

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 get access to. This is the first of a series of episodes in which I will speak about the technology and the challenges we are facing while we build it. Happy listening and stay tuned!

In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future. Join us to our Discord channel to discuss your favorite episode and propose new ones. I would like to thank all of you for supporting and inspiring us. I wish you a wonderful 2020! Francesco and the team of Data Science at Home

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 machine C: Francesco, today we are starting with an infuriating discussion. Are you ready to be angry? F: yeah sure is this about brexit? No, I don’t talk about that. In 1986 the New York City’s Rockefeller University conducted a study on breast and uterine cancers and their link to obesity. Like in all clinical trials up to that point, the subjects of the study were all men. So Francesco, do you see a problem with this approach? F: No problem at all, as long as those men had a perfectly healthy uterus. In medicine, up to the end of the 20th century, medical studies and clinical trials were conducted on men, medicine dosage and therapy calculated on men (white men). The female body has historically been considered an exception, or variation, from a male body. F: Like Eve coming from Adam’s rib. I thought we were past that... When the female body has been under analysis, the focus was on the difference between it and the male body, the so-called “bikini approach”: the reproductive organs are different, therefore we study those, and those only. For a long time medicine assumed this was the only difference. Oh good ... This has led to a hugely harmful fallout across society. Because women had reproductive organs, they should reproduce, and all else about them was deemed uninteresting. Still today, they consider a woman without children somehow to have betrayed her biological destiny. This somehow does not apply to a man without children, who also has reproductive organs. F: so this is an example of a very specific type of bias in medicine, regarding clinical trials and medical studies, that is not only harmful for the purposes of these studies, but has ripple effects in all of society Only in the 2010 a serious conversation has started about the damage caused by not including women in clinical trials. There are many many examples (which we list in the references for this episode). Give me one Researchers consider cardiovascular disease a male disease - they even call it “the widower”. They conduct studies on male samples. But it turns out, the symptoms of a heart attack, especially the ones leading up to one, are different in women. This led to doctors not recognising or dismissing the early symptoms in women. F: I was reading that women are also subject to chronic pain much more than men: for example migraines, and pain related to endometriosis. But there is extensive evidence now of doctors dismissing women’s pain, as either imaginary, or “inevitable”, like it is a normal state of being and does not need a cure at all. The failure of the medical community as a whole to recognise this obvious bias up to the 21st century is an example of how insidious the problem of bias is. There are 3 fundamental types of bias: One: Stochastic drift: you train your model on a dataset, and you validate the model on a split of the training set. When you apply your model out in the world, you systematically add bias in the predictions due to the training data being too specific Two: The bias in the model, introduced by your choice of the parameters of your model. Three: The bias in your training sample: people put training samples together, and people have culture, experience, and prejudice. As we will see today, this is the most dangerous and subtle bias. Today we’ll talk about this bias. Bias is a warping of our understanding of reality. We see reality through the lens of our experience and our culture. The origin of bias can date back to traditions going back centuries, and is so ingrained in our way of thinking, that we don’t even see it anymore. F: And let me add, when it comes to machine learning, we see reality through the lens of data. Bias is everywhere, and we could spend hours and hours talking about it. It’s complicated. It’s about to become more complicated. F: of course, if I know you… Let’s throw artificial intelligence in the mix. F: You know, there was a happier time when this sentence didn’t fill me with a sense of dread... ImageNet is an online database of over 14 million photos, compiled more than a decade ago at Stanford University. They used it to train machine learning algorithms for image recognition and computer vision, and played an important role in the rise of deep learning. We’ve all played with it, right? The cats and dogs classifier when learning Tensorflow? (I am a dog by the way. ) F: ImageNet has been a critical asset for computer-vision research. There was an annual international competition to create algorithms that could most accurately label subsets of images. In 2012, a team from the University of Toronto used a Convolutional Neural Network to handily win the top prize. That moment is widely considered a turning point in the development of contemporary AI. The final year of the ImageNet competition was 2017, a

Get in touch with us Join the discussion about data science, machine learning and artificial intelligence on our Discord server Episode transcript We always hear the word “metadata”, usually in a sentence that goes like this Your Honor, I swear, we were not collecting users data, just metadata. Usually the guy saying this sentence is Zuckerberg, but could be anybody from Amazon or Google. “Just” metadata, so no problem. This is one of the biggest lies about the reality of data collection. F: Ok the first question is, what the hell is metadata? Metadata is data about data. F: Ok… still not clear. Imagine you make a phone call to your mum. How often do you call your mum, Francesco? F: Every day of course! (coughing) Good boy! Ok, so let’s talk about today’s phone call. Let’s call “data” the stuff that you and your mum actually said. What did you talk about? F: She was giving me the recipe for her famous lasagna. So your mum’s lasagna is the DATA. What is the metadata of this phone call? The lasagna has data of its own attached to it: the date and time when the conversation happened, the duration of the call, the unique hardware identifiers of your phone and your mum’s phone, the identifiers of the two sim cards, the location of the cell towers that pinged the call, the GPS coordinates of the phones themselves. F: yeah well, this lasagna comes with a lot of data :) And this is assuming that this data is not linked to any other data like your Facebook account or your web browsing history. More of that later. F: Whoa Whoa Whoa, ok. Let’s put a pin in that. Going back to the “basic” metadata that you describe. I think we understand the concept of data about data. I am sure you did your research and you would love to paint me a dystopian nightmare, as always. Tell us why is this a big deal? Metadata is a very big deal. In fact, metadata is far more “useful” than the actual data, where by “useful” I mean that it allows a third party to learn about you and your whole life. What I am saying is, the fact that you talk with your mum every day for 15 minutes is telling me more about you than the content of the actual conversations. In a way, the content does not matter. Only the metadata matters. F: Ok, can you explain this point a bit more? Imagine this scenario: you work in an office in Brussels, and you go by car. Every day, you use your time in the car while you go home to call your mum. So every day around 6pm, a cell tower along the path from your office to your home pings a call from your phone to your mum’s phone. Someone who is looking at your metadata, knows exactly where you are while you call your mum. Every day you will talk about something different, and it doesn't really matter. Your location will come through loud and clear. A lot of additional information can be deduced from this too: for example, you are moving along a motorway, therefore you have a car. The metadata of a call to mum now becomes information on where you are at 6pm, and the way you travel. F: I see. So metadata about the phone call is, in fact, real data about me. Exactly. YOU are what is interesting, not your mum’s lasagna. F: you say so because you haven’t tried my mum’s lasagna. But I totally get your point. Now, imagine that one day, instead of going straight home, you decide to go somewhere else. Maybe you are secretly looking for another job. Your metadata is recording the fact that after work you visit the offices of a rival company. Maybe you are a journalist and you visit your anonymous source. Your metadata records wherever you go, and one of these places is your secret meeting with your source. Anyone’s metadata can be combined with yours. There will be someone who was with you at the time and place of your secret meeting. Anyone who comes in contact with you can be tagged and monitored. Now their anonymity has been reduced. F: I get it. So, compared to the content of my conversation, its metadata contains more actionable information. And this is the most useful, and most precious, kind of information about me. What I do, what I like, who I am, beyond the particular conversation. Precisely. If companies like Facebook or the phone companies had the explicit permission to collect all the users’ data, including all content of conversations, it’s still the metadata that would generate the most actionable information. They would probably throw the content of conversations away. In the vast majority of instances, the content does not matter. Unless you are an actual spy talking about state secrets, nobody cares. F: Let’s stay on the spy point for a minute. One could say, So what? As I have heard this many times. So what if my metadata contains actionable information, and there are entities that collect it. If I am an honest person, I have nothing to hide. There are two aspects to the problem of privacy. Government surveillance, and corporate - in other words private - surveillance. Government surveillance is a topic that has been c

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 different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates, like environmental issues, abortion issues and so on. In simple terms, someone who is pro-environment, pro-abortion, left-leaning, is also supportive of the Black Lives Matter movement, and viceversa. F: Ok, this seems to make sense, right? But… I suspect there is more to this story? So far, yes…. What they did not expect to find, though, was a pervasive network of Russian accounts participating in the debate, which turned out to be orchestrated by the Internet Research Agency, the not-so-secret Russian secret service agency of internet black ops. The same connected with the US election and Brexit referendum, allegedly. F: Are we talking about actual spies? Where are you going with this? Basically, the Russian accounts (part of them human and part of them bots) were infiltrating all aspects of the debate, both on the left and on the right side, and always taking the most extreme stances on any particular aspect of the debate. The aim was to radicalise the conversation, to make it more and more extreme, in a tactic of divide-and-conquer: turn the population against itself in an online civil war, push for policies that normally would be considered too extreme (for instance, give tanks to the police to control riots, force a curfew, try to ban Muslims from your country). Chaos and unrest have repercussions on international trade and relations, and can align to foreign interests. F: It seems like a pretty indirect and convoluted way of influencing a foreign power… You might think so, but you are forgetting social media. This sort of operation is directly exploiting a core feature of internet social media platforms. And that feature, I am afraid, is recommender systems. F: Whoa. Let’s take a step back. Let’s recap the general features of recommender systems, so we are on the same page. The main purpose of recommender systems is to recommend people the same items similar people show an interest in. Let’s think about books and readers. The general idea is to find a way to predict the best book to the best reader. Amazon is doing it, Netflix is doing it, probably the bookstore down the road does that too, just on a smaller scale. Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors. The major issue of recommender systems is in their validation. Even though validation occurs in a way that is similar to many machine learning methods, one should recommend a set of items first (in production) and measure the efficacy of such a recommendation. But, recommending is already altering the entire scenario, a bit in the flavour of the Heisenberg principle of uncertainty. F: In the attention economy, the business model is to monetise the time the user spends on a platform, by showing them ads. Recommender systems are crucial for this purpose. Chiara, you are saying that these algorithms have effects that are problematic? As you say, recommender systems exist because the business model of social media platforms is to monetise attention. The most effective way to keep users’ attention is to show them stuff they could show an interest in. In order to do that, one must segment the audience to find the best content for each user. But then, for each user, how do you keep them engaged, and make them consume more content? F: You’re going to say the word “filter bubble” very soon. Spot on. To keep the user on the platform, you start by showing them content that they are interested in, and that agrees with their opinion. But that is not all. How many videos of the same stuff can you watch, how many articles can you read? You must also escalate the content that the user sees, increasing the wow factor. The content goes from mild to extreme (conspiracy theories, hate speech etc). The recommended content pushes the user opinion towards more extreme stances. It is hard to see from inside the bubble, but a simple experiment will show it. If you continue to click the first recommended video on YouTube, and you follow the chain of first recommended videos, soon you will find yourself watching stuff you’d never have actively looked for, like conspiracy theories, or alt-right propaganda (or pranks that get progressively more cruel, videos by people committing suicide, and so on). F: So you are saying that this is not an accident: is this the basis of the optimisation of the recommender system? Yes, and it’s very effective. But obviously there are consequences. F: And I’m guessing they are not good. The collective result of

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 feedback loops that we have created are destroying how society works ”. He was referring to how social media platforms leverage our neurological build-up in the same way slot machines and cocaine do, to keep us using their products as much as possible. They turn us into addicts. F: how many times do you check your Facebook in a day? I am not a fan of Facebook. I do not have it on my phone. Still, I check it in the morning on my laptop, and maybe twice more per day. I have a trick though: I do not scroll down. I only check the top bar to see if someone has invited me to an event, or contacted me directly. But from time to time, this resolution of mine slips, and I catch myself scrolling down, without even realising it! F: is it the first thing you check when you wake up? No because usually I have a message from you!! :) But yes, while I have my coffee I do a sweep on Facebook and twitter and maybe Instagram, plus the news. F: Check how much time you spend on Facebook And then sum it up to your email, twitter, reddit, youtube, instagram, etc. (all viable channels for ads to reach you) We have an answer. More on that later. Clearly in this episode there is some form of addiction we would like to talk about. So let’s start from the beginning: how does addiction work? Dopamine is a hormone produced by our body, and in the brain it works as a neurotransmitter, a chemical that neurons use to transmit signals to each other. One of the main functions of dopamine is to shape the “reward-motivated behaviour”: this is the way our brain learns through association, positive reinforcement, incentives, and positively-valenced emotions, in particular, pleasure. In other words, it makes our brain desire more of the things that make us feel good. These things can be for example good food, sex, and crucially, good social interactions, like hugging your friends or your baby, or having a laugh together. Because we are evolved to be social animals with complex social structures, successful social interactions are an evolutionary advantage, and therefore they trigger dopamine release in our brain, which makes us feel good, and reinforces the association between the action and the reward. This feeling motivates us to repeat the behaviour. F: now that you mention reinforcement, I recall that this mechanism is so powerful and effective that in fact we have been inspired by nature and replicated it in-silico with reinforcement learning. The idea is to motivate (and eventually create an addictive pattern) an agent to follow what is called the optimal policy by giving it positive rewards or punishing it when things don’t go the way we planned. In our brain, every time an action produces a reward, the connection between action and reward becomes stronger. Through reinforcement, a baby learns to distinguish a cat from a dog, or that fire hurts (that was me). F: and so this means that all the social interactions people get from social media platforms are in fact doing the same, right? Yes, but with a difference: smartphones in our pockets keep us connected to an unlimited reserve of constant social interactions. This constant flux of notifications - the rewards - flood our brain with dopamine. The mechanism of reinforcement can spin out of control. The reward pathways in our brain can malfunction, and this leads to addiction. F: you are saying that social media has LITERALLY the effect of a drug? Yes. In fact, social media platforms are DESIGNED to exploit the rewards systems in our brain. They are designed to work like a drug. Have you been to a casino and played roulette or the slot machines? F: ...maybe? Why is it fun to play roulette? The fun comes from the WAIT before the reward. You put a chip on a number, you don’t know how it’s going to go. You wait for the ball to spin, you get excited. And from time to time, BAM! Your number comes out. Now, compare this with posting something on facebook. You write a message into the void, wait…. And then the LIKES start coming in. F: yeah i find that familiar... Contrary to the casino, social media platforms do not want our money, in fact they are free. What they want is, and what we are buying into with, is our time. Because the longer we stay on, the longer they can show us ads, and the more money advertisers can pay them. This is no accident, this is the business model. But asking for our time out loud would not work, we would probably not consciously give it to them. So, like a casino, they make it hard for us to get off, once we are on: they make us crave the likes, the right-swipes, the retweets, the subscriptions. So we check in, we stay on, we keep scrolling, because we hope to get those rewards. The short-term satisfaction of getting a “like” is a little boost of dopamine in our brain. We get used t

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 already known to the community: self-attention. In this episode I explain what these mechanisms are, how they work and why they are so powerful. Don't forget to subscribe to our Newsletter or join the discussion on our Discord server References Attention is all you need https://arxiv.org/abs/1706.03762 The illustrated transformer https://jalammar.github.io/illustrated-transformer Self-attention for generative models http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture14-transformers.pdf

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 the training procedure, mode collapse and vanishing gradients. In this episode I not only explain the most challenging issues one would encounter while designing and training Generative Adversarial Networks. But also some methods and architectures to mitigate them. In addition I elucidate the three specific strategies that researchers are considering to improve the accuracy and the reliability of GANs. The most tedious issues of GANs Convergence to equilibrium A typical GAN is formed by at least two networks: a generator G and a discriminator D. The generator's task is to generate samples from random noise. In turn, the discriminator has to learn to distinguish fake samples from real ones. While it is theoretically possible that generators and discriminators converge to a Nash Equilibrium (at which both networks are in their optimal state), reaching such equilibrium is not easy. Vanishing gradients Moreover, a very accurate discriminator would push the loss function towards lower and lower values. This in turn, might cause the gradient to vanish and the entire network to stop learning completely. Mode collapse Another phenomenon that is easy to observe when dealing with GANs is mode collapse. That is the incapability of the model to generate diverse samples. This in turn, leads to generated data that are more and more similar to the previous ones. Hence, the entire generated dataset would be just concentrated around a particular statistical value. The solution Researchers have taken into consideration several approaches to overcome such issues. They have been playing with architectural changes, different loss functions and game theory. Listen to the full episode to know more about the most effective strategies to build GANs that are reliable and robust. Don't forget to join the conversation on our new Discord channel. See you there!

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 memorisation and generalisation in deep learning, with Stanislaw Jastrzębski from New York University. Stan spent two summers as a visiting student with Prof. Yoshua Bengio and has been working on Understanding and improving how deep network generalise Representation Learning Natural Language Processing Computer Aided Drug Design What makes deep learning unique? I have asked him a few questions for which I was looking for an answer for a long time. For instance, what is deep learning bringing to the table that other methods don’t or are not capable of? Stan believe that the one thing that makes deep learning special is representation learning. All the other competing methods, be it kernel machines, or random forests, do not have this capability. Moreover, optimisation (SGD) lies at the heart of representation learning in the sense that it allows finding good representations. What really improves the training quality of a neural network? We discussed about the accuracy of neural networks depending pretty much on how good the Stochastic Gradient Descent method is at finding minima of the loss function. What would influence such minima? Stan's answer has revealed that training set accuracy or loss value is not that interesting actually. It is relatively easy to overfit data (i.e. achieve the lowest loss possible), provided a large enough network, and a large enough computational budget. However, shape of the minima, or performance on validation sets are in a quite fascinating way influenced by optimisation. Optimisation in the beginning of the trajectory, steers such trajectory towards minima of certain properties that go much further than just training accuracy. As always we spoke about the future of AI and the role deep learning will play. I hope you enjoy the show! Don't forget to join the conversation on our new Discord channel. See you there! References Homepage of Stanisław Jastrzębski https://kudkudak.github.io/ A Closer Look at Memorization in Deep Networks https://arxiv.org/abs/1706.05394 Three Factors Influencing Minima in SGD https://arxiv.org/abs/1711.04623 Don't Decay the Learning Rate, Increase the Batch Size https://arxiv.org/abs/1711.00489 Stiffness: A New Perspective on Generalization in Neural Networks https://arxiv.org/abs/1901.09491

In this episode I speak with Jon Krohn, author of Deeplearning Illustrated a book that makes deep learning easier to grasp. We also talk about some important guidelines to take into account whenever you implement a deep learning model, how to deal with bias in machine learning used to match jobs to candidates and the future of AI. You can purchase the book from informit.com/dsathome with code DSATHOME and get 40% off books/eBooks and 60% off video training

Join the discussion on our Discord server In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images. Enjoy the show! References Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.01376

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 already known to the community: self-attention. In this episode I explain what these mechanisms are, how they work and why they are so powerful. Don't forget to subscribe to our Newsletter or join the discussion on our Discord server References Attention is all you need https://arxiv.org/abs/1706.03762 The illustrated transformer https://jalammar.github.io/illustrated-transformer Self-attention for generative models http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture14-transformers.pdf

Join the discussion on our Discord server In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published. We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind! Enjoy the show! References Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron) Pix2Pix: https://phillipi.github.io/pix2pix/ CycleGAN: https://junyanz.github.io/CycleGAN/ GANimorph Paper: https://arxiv.org/abs/1808.04325 Code: https://github.com/brownvc/ganimorph UNIT:https://arxiv.org/abs/1703.00848 MUNIT:https://github.com/NVlabs/MUNIT DRIT: https://github.com/HsinYingLee/DRIT GPT-2 and related Try OpenAI's GPT-2: https://talktotransformer.com/ Blogpost: https://blog.usejournal.com/opengpt-2-we-replicated-gpt-2-because-you-can-too-45e34e6d36dc The Original Transformer Paper: https://arxiv.org/abs/1706.03762 Grover: The FakeNews generator and detector: https://rowanzellers.com/grover/

Join the discussion on our Discord server After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here. In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions. RL seems to work so well! What is wrong with it? Are you a listener of Data Science at Home podcast? A reader of the Amethix Blog? Or did you subscribe to the Artificial Intelligence at your fingertips newsletter? In any case let’s stay in touch! https://amethix.com/survey/ References Emergence of Locomotion Behaviours in Rich Environments https://arxiv.org/abs/1707.02286 Rainbow: Combining Improvements in Deep Reinforcement Learning https://arxiv.org/abs/1710.02298 AlphaGo Zero: Starting from scratch https://deepmind.com/blog/article/alphago-zero-starting-scratch

Join the discussion on our Discord server In this episode I have an amazing conversation with Jimmy Soni and Rob Goodman, authors of “A mind at play”, a book entirely dedicated to the life and achievements of Claude Shannon. Claude Shannon does not need any introduction. But for those who need a refresh, Shannon is the inventor of the information age. Have you heard of binary code, entropy in information theory, data compression theory (the stuff behind mp3, mpg, zip, etc.), error correcting codes (the stuff that makes your RAM work well), n-grams, block ciphers, the beta distribution, the uncertainty coefficient? All that stuff has been invented by Claude Shannon :) Articles: https://medium.com/the-mission/10-000-hours-with-claude-shannon-12-lessons-on-life-and-learning-from-a-genius-e8b9297bee8f https://medium.com/the-mission/on-claude-shannons-103rd-birthday-here-are-103-memorable-claude-shannon-quotes-maxims-and-843de4c716cf?source=your_stories_page--------------------------- http://nautil.us/issue/51/limits/how-information-got-re_invented http://nautil.us/issue/50/emergence/claude-shannon-the-las-vegas-cheat Claude's papers: https://medium.com/the-mission/a-genius-explains-how-to-be-creative-claude-shannons-long-lost-1952-speech-fbbcb2ebe07f http://www.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf A mind at play (book links): http://amzn.to/2pasLMz -- Hardcover https://amzn.to/2oCfVL0 -- Audio

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 more and more attacks to ML systems. In this episode we talk about the most popular attacks against machine learning systems and some mitigations designed by researchers Ambra Demontis and Marco Melis, from the University of Cagliari (Italy). The guests are also the authors of SecML, an open-source Python library for the security evaluation of Machine Learning (ML) algorithms. Both Ambra and Marco are members of research group PRAlab, under the supervision of Prof. Fabio Roli. SecML Contributors Marco Melis (Ph.D Student, Project Maintainer, https://www.linkedin.com/in/melismarco/) Ambra Demontis (Postdoc, https://pralab.diee.unica.it/it/AmbraDemontis) Maura Pintor (Ph.D Student, https://it.linkedin.com/in/maura-pintor) Battista Biggio (Assistant Professor, https://pralab.diee.unica.it/it/BattistaBiggio) References SecML: an open-source Python library for the security evaluation of Machine Learning (ML) algorithms https://secml.gitlab.io/. Demontis et al., “Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks,” presented at the 28th USENIX Security Symposium (USENIX Security 19), 2019, pp. 321–338. https://www.usenix.org/conference/usenixsecurity19/presentation/demontis W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” in International Conference on Machine Learning (ICML), 2017. https://arxiv.org/abs/1703.04730 Melis, A. Demontis, B. Biggio, G. Brown, G. Fumera, and F. Roli, “Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 751–759. https://arxiv.org/abs/1708.06939 Biggio and F. Roli, “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,” Pattern Recognition, vol. 84, pp. 317–331, 2018. https://arxiv.org/abs/1712.03141 Biggio et al., “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Part III, 2013, vol. 8190, pp. 387–402. https://arxiv.org/abs/1708.06131 Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in 29th Int’l Conf. on Machine Learning, 2012, pp. 1807–1814. https://arxiv.org/abs/1206.6389 Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, “Adversarial classification,” in Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, 2004, pp. 99–108. https://dl.acm.org/citation.cfm?id=1014066 Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. "Axiomatic attribution for deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. https://arxiv.org/abs/1703.01365 Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). https://arxiv.org/abs/1606.05386 Guo, Wenbo, et al. "Lemna: Explaining deep learning based security applications." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2018. https://dl.acm.org/citation.cfm?id=3243792 Bach, Sebastian, et al. "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS one 10.7 (2015): E0130140. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140

Join the discussion on our Discord server Scaling technology and business processes are not equal. Since the beginning of the enterprise technology, scaling software has been a difficult task to get right inside large organisations. When it comes to Artificial Intelligence and Machine Learning, it becomes vastly more complicated. In this episode I propose a framework - in five pillars - for the business side of artificial intelligence.

Join the discussion on our Discord server In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published. We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind! Enjoy the show! References Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron) Pix2Pix: https://phillipi.github.io/pix2pix/ CycleGAN: https://junyanz.github.io/CycleGAN/ GANimorph Paper: https://arxiv.org/abs/1808.04325 Code: https://github.com/brownvc/ganimorph UNIT:https://arxiv.org/abs/1703.00848 MUNIT:https://github.com/NVlabs/MUNIT DRIT: https://github.com/HsinYingLee/DRIT GPT-2 and related Try OpenAI's GPT-2: https://talktotransformer.com/ Blogpost: https://blog.usejournal.com/opengpt-2-we-replicated-gpt-2-because-you-can-too-45e34e6d36dc The Original Transformer Paper: https://arxiv.org/abs/1706.03762 Grover: The FakeNews generator and detector: https://rowanzellers.com/grover/

Join the discussion on our Discord server 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 networks faster by squeezing the hardware to their needs and making the training pipeline more dense. Enjoy the show! References Faster Neural Network Training with Data Echoing https://arxiv.org/abs/1907.05550

Join the discussion on our Discord server In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images. Enjoy the show! References Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.01376

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 applications and other series-processing based applications in a multiprocessing environment. I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github References Videflow Github official repository https://github.com/videoflow/videoflow

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 neural networks and some impressive findings about the way they work. The questions that Charles answers in the show are essentially two: Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML? How can we dominate DNN in a theoretically principled way? References The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher Slack channel https://weightwatcherai.slack.com/ Dr. Charles Martin Blog http://calculatedcontent.com and channel https://www.youtube.com/c/calculationconsulting Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning - Charles H. Martin, Michael W. Mahoney

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 highlight how one can build a similarity graph and then run a community detection algorithm on such graph to find clusters in tabular data. You can find a simple hands-on code snippet to play with on the Amethix Blog Enjoy the show! References [1] S. Fortunato, “Community detection in graphs”, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010. [2] Z. Yang, et al., “A Comparative Analysis of Community Detection Algorithms on Artificial Networks”, Scientific Reports volume 6, Article number: 30750 (2016) [3] S. Dongen, “A cluster algorithm for graphs”, Technical Report, CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands, 2000. [4] A. J. Enright, et al., “An efficient algorithm for large-scale detection of protein families”, Nucleic Acids Research, volume 30, issue 7, pages 1575-1584, 2002.

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 comparison between the two and explain what I believe is the best choice for your machine learning project. An interesting post to read (mentioned in the episode) is How businesses can scale Artificial Intelligence & Machine Learning https://amethix.com/how-businesses-can-scale-artificial-intelligence-machine-learning/

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 networks faster by squeezing the hardware to their needs and making the training pipeline more dense. Enjoy the show! References Faster Neural Network Training with Data Echoing https://arxiv.org/abs/1907.05550

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 neural networks and some impressive findings about the way they work. The questions that Charles answers in the show are essentially two: Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML? How can we dominate DNN in a theoretically principled way? References The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher Slack channel https://weightwatcherai.slack.com/ Dr. Charles Martin Blog http://calculatedcontent.com and channel https://www.youtube.com/c/calculationconsulting Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning - Charles H. Martin, Michael W. Mahoney

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 applications and other series-processing based applications in a multiprocessing environment. I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github References Videflow Github official repository https://github.com/videoflow/videoflow

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 in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”. After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.

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 the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms. There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more. Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount. References Twitter https://twitter.com/davekopec GitHub https://github.com/davecom classicproblems.com

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. The method I introduce here is called self-supervised learning. Enjoy the show! Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise! References Deep Clustering for Unsupervised Learning of Visual Features Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

The successes of deep learning for text analytics, also introduced in a recent post about sentiment analysis and published here are undeniable. Many other tasks in NLP have also benefitted from the superiority of deep learning methods over more traditional approaches. Such extraordinary results have also been possible due to the neural network approach to learn meaningful character and word embeddings, that is the representation space in which semantically similar objects are mapped to nearby vectors. All this is strictly related to a field one might initially find disconnected or off-topic: biology. Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise! References [1] Rives A., et al., “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”, biorxiv, doi: https://doi.org/10.1101/622803 [2] Vaswani A., et al., “Attention is all you need”, Advances in neural information processing systems, pp. 5998–6008, 2017. [3] Bahdanau D., et al., “Neural machine translation by jointly learning to align and translate”, arXiv, http://arxiv.org/abs/1409.0473.

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. There is one specific activity that many business owners have been contemplating over the last five years, that is identifying the social sentiment of their brand, by analysing the conversations of their users. In this episode I explain how one can get the best shot at classifying sentences with deep learning and word embedding. Additional material Schematic representation of how to learn a word embedding matrix E by training a neural network that, given the previous M words, predicts the next word in a sentence. Word2Vec example source code https://gist.github.com/rlangone/ded90673f65e932fd14ae53a26e89eee#file-word2vec_example-py References [1] Mikolov, T. et al., "Distributed Representations of Words and Phrases and their Compositionality", Advances in Neural Information Processing Systems 26, pages 3111-3119, 2013. [2] The Best Embedding Method for Sentiment Classification, https://medium.com/@bramblexu/blog-md-34c5d082a8c5 [3] The state of sentiment analysis: word, sub-word and character embedding https://amethix.com/state-of-sentiment-analysis-embedding/

In this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnog Alexandr has written very interesting posts about time series analysis for financial data. His blog is in my personal list of best tutorial blogs. We discuss about financial time series and machine learning, what makes predicting the price of stocks a very challenging task and why machine learning might not be enough. As usual, I ask Alexandr how he sees machine learning in the next 10 years. His answer - in my opinion quite futuristic - makes perfect sense. You can contact Alexandr on Twitter https://twitter.com/AlexRachnog Facebook https://www.facebook.com/rachnog Medium https://medium.com/@alexrachnog Enjoy the show!

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 in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”. After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.

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 machine learning? To find out, listen to the show. References Entropy in machine learning https://amethix.com/entropy-in-machine-learning/

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 Manning Publications at https://www.manning.com/livevideo/deep-learning-crash-course Oliver (Twitter: @DJCordhose) is a veteran of neural networks and machine learning. In addition to the course - that teaches you concepts from prototype to production - he's working on a really cool project that predicts something people do every day... clicking their mouse. If you use promo code poddatascienceathome19 you get a 40% discount for all products on the Manning platform Enjoy the show! References: Deep Learning Crash Course (Manning Publications) https://www.manning.com/livevideo/deep-learning-crash-course?a_aid=djcordhose&a_bid=e8e77cbf Companion notebooks for the code samples of the video course "Deep Learning Crash Course" https://github.com/DJCordhose/deep-learning-crash-course-notebooks/blob/master/README.md Next-button-to-click predictor source code https://github.com/DJCordhose/ux-by-tfjs

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 discussed about the technique they used and some consequences of their findings. They published their paper on Arxiv and made their source code available at https://gitlab.com/EAVISE/adversarial-yolo Enjoy the show! References Fooling automated surveillance cameras: adversarial patches to attack person detection Simen Thys, Wiebe Van Ranst, Toon Goedemé Eavise Research Group KULeuven (Belgium) https://iiw.kuleuven.be/onderzoek/eavise