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Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures
Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost 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.
What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is forThis book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Table of ContentsLinear AlgebraVector CalculusProbability and StatisticsOptimizationGraph TheoryLinear Neural NetworksFeedforward Neural NetworksRegularizationConvolutional Neural NetworksRecurrent Neural NetworksAttention MechanismsGenerative ModelsTransfer and Meta LearningGeometric Deep Learning
Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost 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.
What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is forThis book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Table of ContentsLinear AlgebraVector CalculusProbability and StatisticsOptimizationGraph TheoryLinear Neural NetworksFeedforward Neural NetworksRegularizationConvolutional Neural NetworksRecurrent Neural NetworksAttention MechanismsGenerative ModelsTransfer and Meta LearningGeometric Deep Learning
- GenresArtificial Intelligence
366 pages, Kindle Edition
Published June 12, 2020
18 people are currently reading
19 people want to read
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Displaying 1 - 2 of 2 reviews
February 24, 2022
Author covers, entire formal definitions required for understanding Deep Learning.
I'd say, he could improve his writing, immensely.
Technical writing is difficult, less engaging with questions are missed.
To engage with the material,
We require asking questions, "Why, What, How, When?"
This Writer focuses on, What, How
I want to know, Why and When most importantly
1. Why is this important? (Intuition)
2. How do you implement this? (Mathematical)
3. What is this? (Describing it)
4. When should we use this? (Pros/Cons of Method)
Deus Vult,
Gottfried
I'd say, he could improve his writing, immensely.
Technical writing is difficult, less engaging with questions are missed.
To engage with the material,
We require asking questions, "Why, What, How, When?"
This Writer focuses on, What, How
I want to know, Why and When most importantly
1. Why is this important? (Intuition)
2. How do you implement this? (Mathematical)
3. What is this? (Describing it)
4. When should we use this? (Pros/Cons of Method)
Deus Vult,
Gottfried
April 30, 2025
Disappointing Learning Resource
I received this book as a gift from a friend, but unfortunately, it didn’t meet my expectations. Rather than teaching or explaining the material in depth, it simply lists mathematical concepts you should know, with little to no clear explanation or guidance. As a learning resource, it falls short. Additionally, I found it to be overpriced for what it offers.
I received this book as a gift from a friend, but unfortunately, it didn’t meet my expectations. Rather than teaching or explaining the material in depth, it simply lists mathematical concepts you should know, with little to no clear explanation or guidance. As a learning resource, it falls short. Additionally, I found it to be overpriced for what it offers.
Displaying 1 - 2 of 2 reviews