The Best Resources on Artificial Intelligence and Machine Learning
By: Michal Lukac
Tags: Artificial Intelligence, Machine Learning, Deep Learning, Startup, Data Science

MACHINE LEARNING GUIDE

The Best Resources on Artificial Intelligence and Machine Learning

AI will change all possible fields whether it is physics, law, or retail and one should be prepared for what is to come…

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Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking. If you are interested in the current progress of AI or you are just curious about what will be the future then you are on the right page. AI will change all possible fields whether it is physics, law, or retail and one should be prepared for what is to come…

Podcasts

If there is one medium that has become popular in recent years, it must be podcasts. Everyone is doing it right now — there are podcasts about sex, politics, tech, healthcare, brains, bicycles,… and AI is not missing. But one of them stands out. It is a podcast by Lex Fridman. This MIT alumni is doing an incredible job by interviewing top people from the field, famous people included (like Garry Kasparov or Elon Musk). Some episodes are more about science, physics, mind, startups, and the future of humanity. The ideas presented in the podcast are just mind-blowing. The talks are deep but clever and it will take you some time to get through them.

The Turing test is a recursive test. The Turing test is a test on us. It is a test of whether people are intelligent enough to understand themselves.

Another great podcast is Brain Inspired by Paul Middlebrooks with interesting guests. It shows and discusses topics from Neuroscience and AI and how these fields are connected together.

Books

Life 3.0 by Max Tegmark — How will AI change healthcare, jobs, justice, or war? Max Tegmark is a professor at MIT who has written this provocative and engaging book about the future. He tries to answer a lot of questions like What is intelligence? Can a machine have a consciousness? Can we control AI? … This is a great introduction even for non-technical people.

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee — Book is about incredible progress in AI in China.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron — Do you know how to code and would you like to start with some experiments? This book is not only about one of the most popular programming framework TensorFlow but also about modern techniques in machine learning and neural networks. You will code your first image recognition model and learn how to preprocess and analyze text.

Deep Learning for Coders with fastai and PyTorch by Jeremy Howard and Sylvain Gugger — Another great book for coders. Code examples are in the PyTorch framework. Jeremy Howard is a famous researcher and developer in the AI community. His fastai project helps millions of people to get into deep learning.

Looking for more hardcore books with math equations? Then try Deep Learning by MIT Press. Are you interested in classic approaches, then many university students will remember preparing for exams with Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig or the Bishop’s Pattern Recognition and Machine Learning. (These two are a bit advanced and many topics are for a master or even Ph.D. level).

Magazines

MIT Technology Review is a great magazine with the latest news and trends in technology and future innovations. The magazine covers also other interesting topics as biotechnology, blockchain, space, climate change, and more. There is a print or digital access option for you.

Popular videos & channels

People to Follow

There are a lot of famous Scientists & Engineers & Entrepreneurs to follow. For example often mentioned Jeremy Howard (fast.ai), Andrej Karpathy (Tesla AI), Yann LeCun (Facebook AI), Rachel Thomas (fast.ai, data ethics), Francois Chollet (Google), Fei-Fei Li (Stanford), Anima Anandkumar (Nvidia AI), Demis Hassabis (DeepMind), Geoffrey Hinton (Google) and more …

Lectures & Online courses

So you’ve read some books and articles and now you want to start digging a little deeper? Or you want to become a Machine Learning Specialist? Then start with some online courses. Of course, you will need to learn a little bit about math before and get some basic programming skills. Online courses are a great option if you can’t study at university or you want to get knowledge at your own pace. Here are some of the courses that can serve you as the start point:

Machine Learning course from Andrew Ng — this one is a classic and most popular one for a number of reasons, it’s great introductory material.

To learn more math we can recommend Mathematics for Machine Learning.

Deep Learning specialization is more about modern approaches of neural networks.

There are a lot of great specializations on Udacity by top companies and engineers from various fields like Healthcare or Automotive.

CS231n and CS224N are great Stanford courses for computer vision and natural language processing (NLP), including video lectures, slides, and materials. It’s FREE!

6.034 and 6.S191 — lectures for AI and Deep Learning by MIT on YouTube.

Practical Deep Learning for Coders by fast.ai — Jeremy Howard is doing a great job here by explaining concepts, ideas and showing the code in Jupiter notebooks.

PyImageSearch — offers great introductory tutorials in the computer vision field

Research blogs

You know how to code and you even know how to build your CNN? Or are you just simply interested in what is the future of the field and how the companies are using AI? Check out some of the latest trends and SOTA approaches from the top research groups in the world. There are several giants like Facebook, Google pushing the AI boundaries:

Great articles

We are always looking for high-quality content that is why some of the following articles can be a bit longer. AI is a complex field which is disrupting the way we live and do business:

Newsletters

Data Science Weekly and Deep Learning Weekly — as the names suggest this is every week news from data science and machine learning.

The Algorithm — a newsletter released by MIT.

The Batch — a newsletter by deeplearning.ai.

Alignment — a newsletter by Rohin Shah.

Trends & Problems

Ethics & Transparency & Safety — Should countries ban the usage of face recognition technology? [source][source] Is ethical to scrape the data from the internet to build your face search startup? [source] What is an unethical use of AI? [source] What about autonomous weapons for defensive purposes? Are social media polarizing people with their clever algorithms optimized for more clicks/likes/…? [source]

Jobs replacement — Will AI replace all manufacturing and basic jobs? Or will the research in AI create even more job opportunities? What is going to do countries that are heavily dependent on manual work labor? [source] Will one day companies using robots/clever algorithms pay AI Tax?

Interpretability & Explainability — Why did the deep learning model predict X and not Y? What the neural network has actually learned? How can we fool the model with adversarial attacks to make it the wrong prediction?

Racial bias in datasets and models — a big issue mostly in Face recognition, Insurance, and Healthcare. [source]

GANs and Deep Fakes — GANs are incredible technology which brings also challenges, … Have you heard about Deep Fakes videos? One day the Deep Fakes will be unrecognizable from genuine content. This could create new problems in politics, business, or our personal lives …

Big and Small models — bigger models can lead to incredible results in NLP [source]. On the other hand, there is also more research to make models lighter and faster with binarization or pruning techniques.

Self-supervised learning — high-quality datasets lead to better results, but building such datasets are expensive and requires a lot of manual labeling work. Maybe one day the AI models will be able to create better internal representations without labels.

That is all for now. There are other great resource lists like the one from DeepMind, from which we got inspired. The list is divided by the level of the target audience — introductory, intermediate, and advanced. We will try to keep this post updated and if we find a gem it will appear here. There is much more material from which you can learn but now it’s up to you to start your own machine learning journey.

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