Understanding Deep Learning
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Understanding Deep Learning
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
Understanding Deep Learning
Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.Topics covered in the book include:- Multilayer Perceptrons- Long- and short-term Memory Networks- Convolutional Neural Networks- AutoencodersEvery topic is thoroughly explained and illustrated graphically. Moreover, implementations in TensorFlow are given for developing a complete understanding.
Introduction to Mathematics for Understanding Deep Learning
Author: Kazuyuki FUJII
language: en
Publisher: Scientific Research Publishing, Inc. USA
Release Date: 2018-08-30
Deep Learning is the heart of Artificial Intelligence and will become a most important field in Data Science in the near future. Deep Learning has attracted much attention recently. It is usually carried out by the gradient descent method, which is not always easy to understand for beginners. When one starts studying Deep Learning first hurdles are (1) how to choose the learning rate (2) how to avoid being trapped by local minima (3) what is a deep meaning of the minibatch. In this book I plan to offer intuitive answers to these questions within my understandings. As a matter of course, when beginners study Deep Learning some mathematical knowledge from Calculus, Linear Algebra, Statistics and Information are required. In the book I gave minimum knowledge required for understanding Deep learning. After reading the book, readers are encouraged to challenge advanced books of Deep Learning (or Artificial Intelligence).