دانلود Packt Google Cloud Machine Learning with TensorFlow آموزش یادگیری ماشین گوگل کلود با تنسورفالو

Packt Google Cloud Machine Learning with TensorFlow Download

Key Features

A quick and easy start with the Google Cloud platform to scale up your training and prediction
Work with various practical examples to train your deep learning training models
Use your trained TensorFlow models to predict for thousands of requests

What You Will Learn

Get access to powerful computers with GPUs organized in clusters to optimize your performance
Train bigger models faster using the Google Cloud infrastructure
Explore machine types and learn how to configure clusters to solve problems
Train deep learning models using the Google Cloud ML Engine
Run classical machine learning algorithms with TensorFlow
Build a state-of-the-art object detection network
Run your trained models to get predictions using the ML Engine API

About

TensorFlow has become the first choice for deep learning tasks because of the way it facilitates building powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction.

This course shows you how to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models.

With this video course, you will use the power of Google's Cloud Platform to train deep neural networks faster. This course supplies various examples of training in Google Cloud ML Engine. You will also learn to run predictions for your model using the cloud. You will explore topics such as cloud infrastructures, convolutional neural networks, training deep networks, model serving, and more.

By the end of the course, you will be expert at training and serving neural models, and beyond.

The code files and related files are placed on GitHub at github.com/PacktPublishing/Google-Cloud-Machine-Learning-with-TensorFlow