Articles by "TensorFlow"

Showing posts with label TensorFlow. Show all posts

Eugene Charniak ... 192 pages - Publisher: The MIT Press; (January, 2019) ... Language: English - AmazonSIN: B07PGRZXN8.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Hisham El-Amir, Mahmoud Hamdy ... 551 pages - Publisher: Apress; (December, 2019) ... Language: English - ASIN: B082ZL1D59 by Amazon.

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you!

What You'll Learn: Develop a deep learning project using data + Study and apply various models to your data + Debug and troubleshoot the proper model suited for your data.

Pramod Singh, Avinash Manure ... 164 pages - Publisher: Apress; (December, 2019) ... Language: English - ASIN: B082X9CM42 by Amazon.

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples: The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.

What You'll Learn: Review the new features of TensorFlow 2.0 + Use TensorFlow 2.0 to build machine learning and deep learning models + Perform sequence predictions using TensorFlow 2.0 + Deploy TensorFlow 2.0 models with practical examples

Sebastian Raschka, Vahid Mirjalili ... 770 pages - Publisher: Packt Publishing; (December, 2019) ... Language: English - ISBN-10: 1789955750 - ISBN-13: 978-1789955750.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn: Master the frameworks, models, and techniques that enable machines to 'learn' from data + Use scikit-learn for machine learning and TensorFlow for deep learning + Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more + Build and train neural networks, GANs, and other models + Discover best practices for evaluating and tuning models + Predict continuous target outcomes using regression analysis + Dig deeper into textual and social media data using sentiment analysis

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