| Description |
1 online resource (316 pages) |
| Physical Medium |
polychrome |
| Description |
text file |
| Contents |
Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; Introducing machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; What is deep learning?; How the human brain works; Deep learning history; Problems addressed; Neural networks; The biological neuron; An artificial neuron; How does an artificial neural network learn?; The backpropagation algorithm; Weights optimization; Stochastic gradient descent; Neural network architectures. |
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Multilayer perceptronDNNs architectures; Convolutional Neural Networks; Restricted Boltzmann Machines; Autoencoders; Recurrent Neural Networks; Deep learning framework comparisons; Summary; Chapter 2: First Look at TensorFlow; General overview; What's new with TensorFlow 1.x?; How does it change the way people use it?; Installing and getting started with TensorFlow; Installing TensorFlow on Linux; Which TensorFlow to install on your platform?; Requirements for running TensorFlow with GPU from NVIDIA; Step 1: Install NVIDIA CUDA; Step 2: Installing NVIDIA cuDNN v5.1+ |
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Step 3: GPU card with CUDA compute capability 3.0+Step 4: Installing the libcupti-dev library; Step 5: Installing Python (or Python3); Step 6: Installing and upgrading PIP (or PIP3); Step 7: Installing TensorFlow; How to install TensorFlow; Installing TensorFlow with native pip; Installing with virtualenv; Installing TensorFlow on Windows; Installation from source; Install on Windows; Test your TensorFlow installation; Computational graphs; Why a computational graph?; Neural networks as computational graphs; The programming model; Data model; Rank; Shape; Data types; Variables; Fetches; Feeds. |
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TensorBoardHow does TensorBoard work?; Implementing a single input neuron; Source code for the single input neuron; Migrating to TensorFlow 1.x; How to upgrade using the script; Limitations; Upgrading code manually; Variables; Summary functions; Simplified mathematical variants; Miscellaneous changes; Summary; Chapter 3: Using TensorFlow on a Feed-Forward Neural Network; Introducing feed-forward neural networks; Feed-forward and backpropagation; Weights and biases; Transfer functions; Classification of handwritten digits; Exploring the MNIST dataset; Softmax classifier; Visualization. |
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How to save and restore a TensorFlow modelSaving a model; Restoring a model; Softmax source code; Softmax loader source code; Implementing a five-layer neural network; Visualization; Five-layer neural network source code; ReLU classifier; Visualization; Source code for the ReLU classifier; Dropout optimization; Visualization; Source code for dropout optimization; Summary; Chapter 4: TensorFlow on a Convolutional Neural Network; Introducing CNNs; CNN architecture; A model for CNNs -- LeNet; Building your first CNN; Source code for a handwritten classifier; Emotion recognition with CNNs. |
| Note |
Source code for emotion classifier. |
| Summary |
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and d... |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
| Genre/Form |
Electronic books.
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| Added Author |
Karim, Md. Rezaul (Computer research scientist)
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Menshawy, Ahmed.
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| Other Form: |
Print version: Zaccone, Giancarlo. Deep Learning with TensorFlow. Birmingham : Packt Publishing, ©2017 |
| ISBN |
9781786460127 (electronic book) |
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1786460122 (electronic book) |
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1786469782 |
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9781786469786 |
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