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Making machine learning easy for everyone

Making machine learning easy for everyone
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United States

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Making machine learning easy for everyone

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English


Episodes

[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan)

10/18/2019
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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...

Duration:00:37:47

What is wrong with reinforcement learning?

10/15/2019
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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...

Duration:00:21:47

Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history

10/10/2019
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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...

Duration:00:32:21

Attacking machine learning for fun and profit (conversation with the authors of SecML)

9/30/2019
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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...

Duration:00:34:04

[RB] How to scale AI in your organisation

9/26/2019
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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.

Duration:00:13:21

Training neural networks faster without GPU [RB]

9/17/2019
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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

Duration:00:22:21

How to generate very large images with GANs

9/6/2019
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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...

Duration:00:14:41

[RB] Complex video analysis made easy with Videoflow

8/29/2019
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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...

Duration:00:30:42

[RB] Validate neural networks without data with Dr. Charles Martin

8/26/2019
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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...

Duration:00:44:46

How to cluster tabular data with Markov Clustering

8/20/2019
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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”,...

Duration:00:20:43

Waterfall or Agile? The best methodology for AI and machine learning

8/14/2019
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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...

Duration:00:14:26

Episode 68: AI and the future of banking with Chris Skinner [RB]

7/9/2019
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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,...

Duration:00:41:42

Episode 67: Classic Computer Science Problems in Python

7/2/2019
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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...

Duration:00:28:35

Episode 66: More intelligent machines with self-supervised learning

6/25/2019
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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...

Duration:00:18:56

Episode 65: AI knows biology. Or does it?

6/23/2019
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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...

Duration:00:12:14

Episode 64: Get the best shot at NLP sentiment analysis

6/14/2019
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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...

Duration:00:12:58

Episode 63: Financial time series and machine learning

6/4/2019
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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...

Duration:00:21:08

Episode 62: AI and the future of banking with Chris Skinner

5/28/2019
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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,...

Duration:00:42:03

Episode 61: The 4 best use cases of entropy in machine learning

5/21/2019
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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/

Duration:00:21:35

Episode 60: Predicting your mouse click (and a crash course in deeplearning)

5/16/2019
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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...

Duration:00:39:50