Category: Data Science / ML

Rating: 8.0/10. An introductory textbook on computer vision using deep learning, assuming no prior knowledge of deep learning; thus, the first half of the book covers the basic concepts of neural network architecture and training setups. The second half is more advanced, focusing specifically on computer vision, and covers topics such as vision CNN architectures,…

Rating: 9.0/10. Overall, great textbook about reinforcement learning using deep neural networks, I liked it because it places roughly equal emphasis on theory and code, there are some equations, but the author explains everything more through intuition rather than formal mathematics, making it easy to understand quickly compared to other textbooks. Many of the algorithms…

Rating: 7.9/10. Book about design patterns specific to machine learning training and productionization. Design patterns are useful since they’re tried-and-tested solutions to reoccurring problems. Even though I’ve used ML in my work for several years, some of these patterns are still new to me. The book is aimed at ML practitioners in the industry and…

Rating: 8.0/10. Part 1: From Data to Visualization All figures should be reproducible from data and code, should not have to make manual adjustments in Illustrator, or you will be dissuaded from updating them, or you may forget how they’re generated. Figures may be “ugly” (aesthetically unpleasing), “bad” (unclear and confusing), or “wrong” (objectively incorrect)….

Rating: 7.3/10. Voice user interfaces like Siri and Alexa have improved in some ways, but in many other ways are similar to IVR phone systems from the 1970s. This book goes through various things to look out for while designing them. For example: Be as brief as possible and use visual mode to display lots…