- Why is it possible to assess that the model suffers from overfitting only by looking at the graph?
- Extend the baseline example to place the matrix multiplication operation on a remote device at IP 192.168.1.12; visualize the result on TensorBoard.
- Is it necessary to have a remote device to place an operation on?
- Extend the CNN architecture defined in the define_cnn method: add a batch normalization layer (from tf.layers) between the output of the convolutional layer and its activation function.
- Try to train the model with the extended CNN architecture: the batch normalization layer adds two update operations that must be executed before running the training operation. Become familiar with the tf.control_dependencies method to force the execution of the operations contained inside the collection tf.GraphKeys.UPDATE_OPS, to be executed before the train operation (look...
Hands-On Neural Networks with TensorFlow 2.0
By :
Hands-On Neural Networks with TensorFlow 2.0
By:
Overview of this book
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.
This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Preface
What is Machine Learning?
Neural Networks and Deep Learning
Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
