The book closes with other forms of learning such as unsupervised, self-supervised, and recommender systems. He explains common challenges, pitfalls as well as targeted solutions. Chapter 6 is dedicated to neural networks.Īfterward, Burkov takes an interesting turn and discusses how one can use the aforementioned methods to solve specific problems. Chapter 4 is all about gradient descent and the learning process, while Chapter 5 is a collection of best practices namely, feature engineering, regularization, hyperparameter tuning, and more. Later on, Burkov analyzes the most important ML algorithms such as Regression, Decision Trees, Support vector machines, and k-Nearest neighbors. The first two chapters focus on machine learning formulation, notation, and key terminology. Unfortunately, this book did not exist when we started learning ML, and thus we had to dig all around for information. If you are not, you will probably find it boring and overlapping with things you already know. If you are a newcomer this is the book for you. The Hundred-Page Machine Learning Book by Andriy Burkov You can choose the one that works best for you! Machine and Deep Learning fundamentals MLOPs: cloud, production, and deep learning engineering. Machine and Deep Learning fundamentals (for beginners).įramework-centered books: Pytorch, Tensorflow and Keras.
Otherwise, feel free to ignore them.Īfter careful consideration, we divided 4 axons of approaching the topic: If you want to support us, feel free to use them. Finally, we include our book (Deep learning in production), not because we have to, but because we sincerely believe it’s worth being on the list.Īlso, note that some of the links below might be affiliate links, and at no additional cost to you, we will earn a commission if you decide to make a purchase after clicking through. In contrast to other “best/top” AI book lists you can find out there, we have spent at least a couple of hours on each book and thus provide an honest review. The reason is simple: ingesting exactly the information you need at that time. Still, we occasionally visit specific chapters or sections. We have probably never finished a single one of those books ourselves. We’ve certainly missed very good candidates but we believe these books are more than enough to fill your time dedicated to reading. The competition is intense and it’s so hard to pick the best ones. Several books focus on deep learning have been written in the last few years.
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