Hands-On Deep Learning for Images with TensorFlow | 577 | Will Ballard | 2018 | Packt Publishing | 9781789538670
Explore TensorFlow's capabilities to perform efficient deep learning on imagesKey Features ✔Discover image processing for machine vision
✔Build an effective image classification system using the power of CNNs
✔Leverage TensorFlow's capabilities to perform efficient deep learning
Book Description TensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.
Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
What you will learn ✔Build machine learning models particularly focused on the MNIST digits
✔Work with Docker and Keras to build an image classifier
✔Understand natural language models to process text and images
✔Prepare your dataset for machine learning
✔Create classical, convolutional, and deep neural networks
✔Create a RESTful image classification server
Who this book is for Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
Deep learning is a group of exciting new technologies for neural networks. Through advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn information hierarchies like the human brain's function. This book will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Transformers, Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning.
This book covers the application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation. The book presents both GPU and CPU processing for deep learning. The focus is primarily on applying deep learning to problems and introducing mathematical foundations as needed. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. Some applications make use of PyTorch.
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️ Applications of Deep Neural Networks with Keras.pdf (26.96 MB)------------------------------------*****------------------------------------
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