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Getting Started with Deep Learning

Artificial intelligence might work, and if it does, it will be the biggest development in technology ever.

– Sam Altman

Welcome to the Hands-On Deep Learning Architectures with Python! If you are completely unfamiliar with deep learning, you can begin your journey right here with this book. And for readers who have an idea about it, we have covered almost every aspect of deep learning. So you are definitely going to learn a lot more about deep learning from this book.

The book is laid out in a cumulative manner; that is, it begins from the basics and builds it over and over to get to advanced levels. In this chapter, we discuss how humans started creating intelligence in machines and how artificial intelligence gradually evolved to machine learning and eventually deep learning. We then see some nice applications of deep learning. Moving...

Key benefits

Explore advanced deep learning architectures using various datasets and frameworks

Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more

Discover design patterns and different challenges for various deep learning architectures

Description

Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.

Who is this book for?

If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

What you will learn

Implement CNNs, RNNs, and other commonly used architectures with Python

Explore architectures such as VGGNet, AlexNet, and GoogLeNet

Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more

Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples

Master artificial intelligence and neural network concepts and apply them to your architecture

Understand deep learning architectures for mobile and embedded systems

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Hands-On Deep Learning Algorithms with Python

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Hands-On Deep Learning Architectures with Python

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Total $ 120.97

andre luis Apr 25, 2022

Este livro faz o balanceamento da teoria e da prática, fazendo a conexão entre ambos na medida certa, nem exagerando na teoria como outras publicações o fazem, nem carecendo de explicação mínima para os comandos utilizados, que são o foco desta obra ( Tensorflow e Keras ).Obviamente é necessário um conhecimento prévio mínimo por parte do leitor sobre o assunto 'redes neurais', e o livro não desperdiça muitas páginas com essa introdução; apresentando assim no máximo uma revisão da teoria.

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Anonymus Jul 31, 2019

All programs are implemented using tensorflow and keras.Some of the algorithms are not explained from the scratchwhich is very important to learn deeplearning indepth

Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

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Hands-On Deep Learning Architectures with Python

Getting Started with Deep Learning

Artificial intelligence

Deep learning

Applications of deep learning

Building the fundamentals

Biological inspiration

TensorFlow and Keras

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Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

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