TensorFlow 2.0 quick start guide : get up to speed with the newly introduced features of TensorFlow 2.0 /: get up to speed with the newly introduced features of TensorFlow 2.0. (2019)

• Record Type: Book
• Other Titles: TensorFlow two point zero quick start guide
• Further Information: Note: Tony Holdroyd.
• Authors: Holdroyd, Tony
• Contents: Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction to TensorFlow 2.00 Alpha; Chapter 1: Introducing TensorFlow 2; Looking at the modern TensorFlow ecosystem; Installing TensorFlow; Housekeeping and eager operations; Importing TensorFlow; Coding style convention for TensorFlow; Using eager execution; Declaring eager variables; Declaring TensorFlow constants; Shaping a tensor; Ranking (dimensions) of a tensor; Specifying an element of a tensor; Casting a tensor to a NumPy/Python variable Finding the size (number of elements) of a tensorFinding the datatype of a tensor; Specifying element-wise primitive tensor operations; Broadcasting; Transposing TensorFlow and matrix multiplication; Casting a tensor to another (tensor) datatype; Declaring ragged tensors; Providing useful TensorFlow operations; Finding the squared difference between two tensors; Finding a mean; Finding the mean across all axes; Finding the mean across columns; Finding the mean across rows ; Generating tensors filled with random values; Using tf.random.normal(); Using tf.random.uniform() Using a practical example of random valuesFinding the indices of the largest and smallest element; Saving and restoring tensor values using a checkpoint; Using tf.function; Summary; Chapter 2: Keras, a High-Level API for TensorFlow 2; The adoption and advantages of Keras; The features of Keras; The default Keras configuration file; The Keras backend; Keras data types; Keras models; The Keras Sequential model; The first way to create a Sequential model; The second way to create a Sequential model; The Keras functional API; Subclassing the Keras Model class; Using data pipelines Saving and loading Keras modelsKeras datasets; Summary; Chapter 3: ANN Technologies Using TensorFlow 2; Presenting data to an ANN; Using NumPy arrays with datasets; Using comma-separated value (CSV) files with datasets; CSV example 1; CSV example 2; CSV example 3; TFRecords; TFRecord example 1; TFRecord example 2; One-hot encoding; OHE example 1; OHE example 2; Layers; Dense (fully connected) layer; Convolutional layer; Max pooling layer; Batch normalization layer and dropout layer; Softmax layer; Activation functions; Creating the model; Gradient calculations for gradient descent algorithms Loss functionsSummary; Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha; Chapter 4: Supervised Machine Learning Using TensorFlow 2; Supervised learning; Linear regression; Our first linear regression example; The Boston housing dataset; Logistic regression (classification); k-Nearest Neighbors (KNN); Summary; Chapter 5: Unsupervised Learning Using TensorFlow 2; Autoencoders; A simple autoencoder; Preprocessing the data; Training; Displaying the results; An autoencoder application - denoising; Setup; Preprocessing the data; The noisy images; Creating the encoding layers
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2019
• Extent: 1 online resource, illustrations
• Subjects: 005.3; Application software -- Development; Neural networks (Computer science); Machine learning; Electronic books
• Languages: English
• ISBNs: 9781789536966; 1789536960
• Related ISBNs: 9781789530759
• Notes: Note: Description based on online resource; title from title page (Safari, viewed May 1, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.