The Turing machine (TM) was proposed by Alan Turing in 1936, and it is a mathematical model of computation made up of an infinitely long tape and a head that interacts with the tape by reading, editing, and moving symbols on it. It works by manipulating symbols on the strip according to a predefined set of rules. The tape is made up of an endless number of cells, each of which can contain one of three symbols – 0, 1, or blank (" "). Therefore, this is referred to as a three-symbol Turing machine. Regardless of how simple it seems, it is capable of simulating any computer algorithm, regardless of complexity. The tape that these computations are done on can be considered to be the machine's memory, akin to how our modern-day computers have memory. However, the Turing machine differs from modern-day computers as it has limited...
Hands-On Mathematics for Deep Learning
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Hands-On Mathematics for Deep Learning
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Overview of this book
Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.
You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.
By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
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Current Title:
Hands-On Mathematics for Deep Learning
Table of Contents (19 chapters)
Preface
Section 1: Essential Mathematics for Deep Learning
Free Chapter
Linear Algebra
Vector Calculus
Probability and Statistics
Optimization
Graph Theory
Section 2: Essential Neural Networks
Linear Neural Networks
Feedforward Neural Networks
Regularization
Convolutional Neural Networks
Recurrent Neural Networks
Section 3: Advanced Deep Learning Concepts Simplified
Attention Mechanisms
Generative Models
Transfer and Meta Learning
Geometric Deep Learning
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