Book Image

Machine Learning with TensorFlow 1.x

By : Quan Hua, Saif Ahmed, Shams Ul Azeem
Book Image

Machine Learning with TensorFlow 1.x

By: Quan Hua, Saif Ahmed, Shams Ul Azeem

Overview of this book

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Table of Contents (13 chapters)
Free Chapter
1
Getting Started with TensorFlow

Dataset and challenges

In this section, we will show you some popular datasets and competitions.

Problem 1 - ImageNet dataset

Project link: http://image-net.org/

ImageNet is a large scale visual recognition challenge that has runs annually since 2010. The dataset is organized according to the WorkNet hierarchy. There are over ten million URLs of images with hand-annotated labels to indicate what objects are in the picture. There are at least one million images that have bounding boxes included.

The ImageNet challenge is held every year to evaluate algorithms for the following three problems:

  • Object localization for 1,000 categories.
  • Object detection for 200 fully-labeled categories.
  • Object detection from video for 30 fully...