Probability Theory: Independence, Exchangeability, Martingalesby Chow and Teicher. Yes, probability is just a measure on sets, but this tour-de-force of a book explains the unique measure-theoretic properties of probability. This book shows you how mathematicians think of probability. I’m guessing...
I think having good references is the fastest way to getting good answers to your machine learning questions, and having multiple books can give you multiple perspectives on tough questions. In this guide, you will discover the top books on machine learning. There are many reasons to want and ...
“Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical backgro...
Machine Learning The gist: A collection of essays on different and highly specific aspects of machine learning. Some are more general and philosophical; others are focused on specific problem domains, such as “Machine Learning Methods for Spoken Dialogue Simulation and Optimization.” Target audience...
Deep Learning with Pythonwas written by a creator of Keras, one of the most popular machine learning libraries in Python. The book starts gently, is very practical, gives pieces of code you can use right away and has in general many useful tips on using deep learning. An absolute must rea...
The book is basically a godsend for those having a loose grip on mathematics. Understanding Machine Learning: From Theory to Algorithms Author: Shai Shalev-Shwartz and Shai Ben-David For the mathematics- savvy people, this is one of the most recommended books for understanding the magic behind ...
Machine Learning became one of the hottest domain of Computer Science. Each larger company is either applying Machine Learning or thinking about doing so soon to solve their problems and understand…
been realized. “A new presence confirmed real”: the reading has installed this presence of body and text and thus the “books” have indeed become “machines.” But the statement also encourages us to reflect on the book as just a “machine” or as Johanna Drucker suggested already in ...
“Machine Learning For Dummies” by John Paul Mueller and Luca Massaron “Machine Learning for Hackers” by Drew Conway and John Myles White "An Introduction to Statistical Learning" by Trevor Hastie and co-authors "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition...
The instance_size is the instance_size_in_words << kPointerSizeLog2 (3 on my machine): (lldb) memory read -f x -s 1 -c 1 *map+8 0x24d1cd509ed1: 0x03 (lldb) expr 0x03 << 3 (int) $2 = 24 (lldb) expr map->instance_size() (int) $3 = 24 i::HeapObject::kHeaderSize...