Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Автор: Boschetti Alberto, Massaron Luca, Thakur Abhishek Название: Tensorflow Deep Learning Projects ISBN: 1788398068 ISBN-13(EAN): 9781788398060 Издательство: Неизвестно Цена: 8458.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks` performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more.
Автор: Laborde Gant Название: Learning Tensorflow.Js: Machine Learning in JavaScript ISBN: 1492090794 ISBN-13(EAN): 9781492090793 Издательство: Wiley Рейтинг: Цена: 7126.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.
Автор: Pattanayak, Santanu Название: Pro deep learning with tensorflow 2.0 ISBN: 1484289307 ISBN-13(EAN): 9781484289303 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. What You Will Learn * Understand full-stack deep learning using TensorFlow 2.0 * Gain an understanding of the mathematical foundations of deep learning * Deploy complex deep learning solutions in production using TensorFlow 2.0 * Understand generative adversarial networks, graph attention networks, and GraphSAGE Who This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.
Автор: McClure Nick Название: Tensorflow Machine Learning Cookbook - Second Edition ISBN: 1789131685 ISBN-13(EAN): 9781789131680 Издательство: Неизвестно Цена: 7539.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook About This Book - Your quick guide to implementing TensorFlow in your day-to-day machine learning activities - Learn advanced techniques that bring more accuracy and speed to machine learning - Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn - Become familiar with the basics of the TensorFlow machine learning library - Get to know Linear Regression techniques with TensorFlow - Learn SVMs with hands-on recipes - Implement neural networks and improve predictions - Apply NLP and sentiment analysis to your data - Master CNN and RNN through practical recipes - Take TensorFlow into production In Detail TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning - each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
Описание: Chapter 1: Optimization and neural networks Subtopics: How to read the book Introduction to the book Chapter 2: Hands-on with One Single NeuronSubtopics: Overview of optimization A definition of learning Constrained vs. unconstrained optimization Absolute and local minima Optimization algorithms with focus on Gradient Descent Variations of Gradient Descent (mini-batch and stochastic) How to choose the right mini-batch size Chapter 3: Feed Forward Neural NetworksSubtopics: A short introduction to matrix algebra Activation functions (identity, sigmoid, tanh, swish, etc.) Implementation of one neuron in Keras Linear regression with one neuron Logistic regression with one neuron Chapter 4: RegularizationSubtopics: Matrix formalism Softmax activation function Overfitting and bias-variance discussion How to implement a fully conneted network with Keras Multi-class classification with the Zalando dataset in Keras Gradient descent variation in practice with a real dataset Weight initialization How to compare the complexity of neural networks How to estimate memory used by neural networks in Keras Chapter 5: Advanced OptimizersSubtopics: An introduction to regularization l_p norm l_2 regularization Weight decay when using regularization Dropout Early Stopping Chapter 6Chapter Title: Hyper-Parameter tuningSubtopics: Exponentially weighted averages Momentum RMSProp Adam Comparison of optimizers Chapter 7Chapter Title: Convolutional Neural NetworksSubtopics: Introduction to Hyper-parameter tuning Black box optimization Grid Search Random Search Coarse to fine optimization Sampling on logarithmic scale Bayesian optimisation Chapter 8Chapter Title: Brief Introduction to Recurrent Neural NetworksSubtopics: Theory of convolution Pooling and padding Building blocks of a CNN Implementation of a CNN with Keras Introduction to recurrent neural networks Implementation of a RNN with Keras Chapter 9: AutoencodersSubtopics: Feed Forward Autoencoders Loss function in autoencoders Reconstruction error Application of autoencoders: dimensionality reduction Application of autoencoders: Classification with latent features Curse of dimensionality Denoising autoencoders Autoencoders with CNN Chapter 10: Metric AnalysisSubtopics: Human level performance and Bayes error Bias Metric analysis diagram Training set overfitting How to split your dataset Unbalanced dataset: what can happen K-fold cross validation Manual metric analysis: an example Chapter 11 Chapter Title: General Adversarial Networks (GANs)Subtopics: Introduction to GANs The building blocks of GANs An example of implementation of GANs in Keras APPENDIX 1: Introduction to KerasSubtopics: Sequential model Keras Layers Funct
Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch
Key Features
Understand the different architectures for image generation, including autoencoders and GANs
Build models that can edit an image of your face, turn photos into paintings, and generate photorealistic images
Discover how you can build deep neural networks with advanced TensorFlow 2.x features
Book Description
The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you'll not only develop image generation skills but also gain a solid understanding of the underlying principles.
Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You'll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You'll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you'll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN.
By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently.
What You Will Learn
Train on face datasets and use them to explore latent spaces for editing new faces
Get to grips with swapping faces with deepfakes
Perform style transfer to convert a photo into a painting
Build and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translation
Use iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic images
Become well versed in attention generative models such as SAGAN and BigGAN
Generate high-resolution photos with Progressive GAN and StyleGAN
Who this book is for
The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You'll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.
Описание: You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Автор: Silaparasetty, Vinita Название: Deep learning projects using tensorflow 2 ISBN: 1484258010 ISBN-13(EAN): 9781484258019 Издательство: Springer Рейтинг: Цена: 9146.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Work through engaging and practical deep learning projects using TensorFlow 2.0.
Автор: Sasaki Kai Название: Hands-On Machine Learning with TensorFlow.js ISBN: 1838821732 ISBN-13(EAN): 9781838821739 Издательство: Неизвестно Рейтинг: Цена: 9378.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Hands-On Machine Learning with TensorFlow.js is a comprehensive guide that will help you easily get started with machine learning algorithms and techniques using TensorFlow.js. By the end of this book, you will be able to create and optimize your own web-based machine learning applications using practical examples.
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines.
Understand the machine learning management lifecycle
Implement data pipelines with Apache Airflow and Kubeflow Pipelines
Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform
Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement
Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js
Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated
Design model feedback loops to increase your data sets and learn when to update your machine learning models
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