Machine Learning With Tensorflow

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter Key Features Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing Cover interesting deep learning solutions for mobile Build your confidence in training models, performance tuning, memory

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

Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js Create smart web applications from scratch with the help of useful examples Use flexible and intuitive APIs from TensorFlow.js to understand

Machine Learning with TensorFlow TensorFlow is a powerful open source software library for performing various numerical data flow graphs. With its powerful resources, TensorFlow is perfect for machine learning enthusiasts offering plenty of workspace where you can improve your machine learning techniques and build your own machine learning algorithms. Thanks

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

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the

Programming With Python - 4 BOOK BUNDLE!! Deep Learning with Keras Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST

Discover more insight about deep learning and how to work with Swift for TensorFlow to develop intelligent apps. TensorFlow was designed for easy adoption by iOS programmers working in Swift. This book covers the established and tested concepts and ties them to modern Swift programming and applicable use in developing

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things