Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition - Image 1

£41.99

Packt Publishing Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

Name: Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
Brand: Packt Publishing
Price: 39.99 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 91 days • 91 data points

Historical

Generating forecast...

Price Distribution

Price distribution over 91 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £40 (85 days, 93.4%)

Price range: £40 - £42

Price levels: 2 different prices over 91 days

Description

Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models - autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended. Table of Contents Introducing Advanced Deep Learning with Keras Deep Neural Networks Autoencoders Generative Adversarial Networks (GANs) Improved GANs Disentangled Representation GANs Cross-Domain GANs Variational Autoencoders (VAEs) Deep Reinforcement Learning Policy Gradient Methods Object Detection Semantic Segmentation Unsupervised Learning Using Mutual Information

Product Specifications

Brand: Packt Publishing
Format: hardcover
ASIN: 1800568274
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 28 February 2020
Listed Since: 16 October 2020

Barcode

No barcode data available

Packt Publishing Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks (Advances in Computer Vision and Pattern Recognition)

Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide

Programming with TensorFlow: Solution for Edge Computing Applications (EAI/Springer Innovations in Communication and Computing)

Deep Reinforcement Learning: Frontiers of Artificial Intelligence

Deep Learning Applications, Volume 3: 1395 (Advances in Intelligent Systems and Computing, 1395)

Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks (Advances in Computer Vision and Pattern Recognition)

Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)

Neural Networks with Model Compression (Computational Intelligence Methods and Applications)

Audio Signal Processing: Advanced Analysis and Machine Learning Applications

The Application of Artificial Intelligence: Step-by-Step Guide from Beginner to Expert: LNAH 2020 (Lecture Notes in Computer Science)

Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biologically Inspired Approaches: 556 (Studies in Computational Intelligence, 556)

An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces (Compact Textbooks in Mathematics)

Big Data Mining and Machine Learning in Geoscience

Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)

Change Detection and Image Time Series Analysis 2: Supervised Methods

Deep Learning with Rust: Mastering Efficient and Safe Neural Networks in the Rust Ecosystem

Big Data for Remote Sensing: Visualization, Analysis and Interpretation: Digital Earth and Smart Earth

Hugging Face in Action

Reshaping Geotechnical Engineering with Machine Learning: Theory, Applications, and Innovations

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II

Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels

Advances in Digital Pathology and Artificial Intelligence in Pathology

Database Management Using AI : A Comprehensive Guide -volume-2

Integrating Artificial Intelligence with Virtual Screening: A New Era in Drug Discovery