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From the Publisher

What’s standing between you and success in deep learning?

To choose the right deep learning (DL) model, train new models, or carry out any number of DL tasks, you need sharp intuition that can only be honed by a firm grasp of the math beneath DL tools and architectures.

Math and Architectures of Deep Learning presents need-to-know theory alongside practical implementations, driving home mathematical concepts behind DL tools, while building programming skills essential for solving real-world problems.

Learning style Explore new fundamental DL concepts or architectural patterns in every chapter Well-annotated Python code lets you look “under the hood” of tools and patterns to see how they work Practice implementing a prototype architecture and train a DL model for a real-world problem Study math, theory, architecture, and code for a wholistic understanding of cutting-edge research

Key Lessons

The Beating Heart of Machine Learning. In Chapter 2, study vectors and matrices from a machine learning perspective, and explore how they factor into the heart of machine learning: number crunching.

Move over, Art… Science can imitate life too! In Chapter 7, discover how the architecture of neural networks differs from what you may be familiar with, examine the role of functions in modeling real-world problems, and learn about a systematic and repeatable method of modeling arbitrarily complex functions.

Sorry for your loss! In Chapter 9, discover components of the optimization process for minimizing loss on your network.

Is it a bird? A plane? Superman? In Chapter 11, take an in-depth look at the various deep learning techniques for object classification and localization that help computers recognize what they “see” in an image.

Setting it Apart

Math and Architectures of Deep Learning delve into the underlying principles of DL, empowering you to build and customize your own DL models.

Author Krishnendu Chaudhury, who earned his hefty DL expertise over decades working for Google and Adobe Systems, presents his unique blend of math, theory, and programming—leading you to mastery of all. In this rewarding guide, you’ll hone the intuition that makes the difference between an implementer of (other people’s) ideas… and a true innovator.

Publisher ‏ : ‎ Manning (March 26, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 552 pages
ISBN-10 ‏ : ‎ 1617296481
ISBN-13 ‏ : ‎ 978-1617296482
Item Weight ‏ : ‎ 2 pounds
Dimensions ‏ : ‎ 7.38 x 1.3 x 9.25 inches

Math and Architectures of Deep Learning

Deep learning has revolutionized the field of artificial intelligence, enabling machines to learn from vast amounts of data and make intelligent decisions. But behind the impressive capabilities of deep learning models lie complex mathematical principles and intricate architectures.

At the heart of deep learning is the concept of neural networks, which are inspired by the structure of the human brain. These networks consist of layers of interconnected nodes, or neurons, that process and transform input data to produce an output. The connections between neurons are represented by weights, which are adjusted during the training process to optimize the model’s performance.

The mathematics behind neural networks involves a range of techniques, including linear algebra, calculus, and probability theory. Matrix multiplication, derivatives, and gradient descent are just a few of the mathematical concepts used to train and fine-tune deep learning models.

In addition to the mathematical principles, the architectures of deep learning models play a crucial role in their performance. Convolutional neural networks (CNNs) are commonly used for image recognition tasks, while recurrent neural networks (RNNs) are suited for sequential data such as text and speech. More advanced architectures, such as transformers and generative adversarial networks (GANs), have further expanded the capabilities of deep learning.

Understanding the math and architectures of deep learning is essential for researchers, developers, and data scientists working in the field. By mastering these principles, they can create more efficient and powerful models that push the boundaries of artificial intelligence.
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