![]() Smola, a book which rightly bills itself as "n interactive deep learning book with code, math, and discussions, based on the NumPy interface." One such exemplar is Dive Into Deep Learning, by Aston Zhang, Zachary C. There are many fine books available for someone looking to go this route, though few offerings tick the boxes of being freely available, up to date, and incredibly thorough. Once you have acquired the requisite mathematical foundations for machine learning, perhaps you are interested in turning your attention to neural networks and deep learning. ![]() For those of us looking to spend some of this idle time learning something new or reviewing something previously learned, we have been (and hope to continue) spotlighting a few select standout textbooks of interest in data science and related fields. ![]() Thanks to the current realities associated with COVID-19, lots of us around the world are spending more time at home than we normally do, and some of us may have additional idle time on ours hands. We think this approach is essential for teaching deep learning because so much of the core knowledge in deep learning is derived from experimentation (vs. Each chapter section teaches a single key idea through multiple modalities, interweaving prose, math, and a self-contained implementation that can easily be grabbed and modified to give your projects a running start. We tried to combine the best aspects of a textbook (clarity and math) with the best aspects of hands-on tutorials (practical skills, reference code, implementation tricks, and intuition). What makes Dive into Deep Learning (D2K) unique is that we went so far with the idea of *learning by doing* that the entire book itself consists of runnable code.
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