Deep Learning Books you should read in 2020

南笙酒味 提交于 2020-02-29 12:03:06

https://towardsdatascience.com/deep-learning-books-you-should-read-in-2020-7806048c1dc5

With the rise of machine learning and data science, applied everywhere and changing every industry, it’s no wonder that experts in machine learning are handsomely paid and much looked after. If you’ve already read a couple of data science and machine learning books, it’s time to focus on deep learning: Neural Networks, Keras, Tensorflow, Scikit-learn, et

Deep Learning books to read in 2020

Introductory level

 

Introduction to Machine Learning with Python is a smooth introduction into machine learning and deep learning. It doesn’t assume any knowledge about coding and Python in particular and it introduces fundamental concepts and applications of machine learning, discussing various methods through examples. That’s the best book I’ve ever seen for an entry level Deep Learning Engineer.

Intermediate level

 

Hands-On Machine Learning with Scikit-Learn and TensorFlow covers all the fundamentals in deep learning, with working code and amazing visualizations full of colours. It’s really fun to read, it is a complete 400+ pages guide through classification, clustering, neural networks and other methods with many examples to try for yourself

Deep Learning with Python is all about using Keras as your primary framework for Deep Learning. Francois Chollet, the creator of Keras, gives a great overview of this easy-to-use and efficient frameworks. From MNIST to CNNs, through computer vision to NLP. All in one place, given in a concise form.

Deep Learning and the Game of Go has as a goal teaching you neural networks and reinforcement learning using Go as a guiding example. During the course of the book, you’ll learn how to create your own bot/agent able to play the game, which is pretty awesome.

Expert level

Deep Learning is a must-read if you’re serious about deep learning. It doesn’t give you code, assuming you’re able to code everything yourself at this stage, but it gives you explanations of why certain layers work better, how to optimize hyperparameters, what network architectures to use, etc. It gives an up-to-date account of deep learning.

Machine Learning: a Probabilistic Perspective is about mathematical perspective on machine learning. Hard to read, but a great reference for any mathematical issues you might have, when you build deep learning models. It’s very useful as an encyclopedic reference. For experts only.


If you’re looking for more book recommendations, see the lists below:

Data Science Job

Finally, if you want to have an overview of what it means to be a Data Scientist, then have a look at my book Data Science Job: How to become a Data Scientist which will guide you through the process.

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