Description
Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what’s new in TensorFlow 2, you’ll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architecturesstarting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you’re already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you’ll see just how wide the range of TensorFlow’s capabilities are. What You’ll Learn Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer Who This Book Is For Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies. Chapter 1: TensorFlow Introduction What’s new in TensorFlow 2 Chapter 2: A Quick Start on TensorFlow Hello World for TensorFlow using Google Colab Chapter 3: TensorFlow Keras Integration tf.keras Image Classification Chapter 4: TensorFlow Hub Transfer Learning TensorFlow Hub and Keras Chapter 5: Regression Predicting Continuous Value Output Chapter 6: Estimators Solving Classification Problems Using Estimators Chapter 7: Distributed Training Describing tf.distribute.Strategy Chapter 8: Text Text Classification Generation with RNN Chapter 9: Language Translation The seq2seq model for language translation Chapter 10: Language Understanding Using Transformer Model Chapter 11: Image Captioning Attention-based model for captioning images Chapter 12: Time Series Forecasting Using RNNs Forecasting a univariate/multivariate time series Chapter 13: Style Transfer Composing an image in the style of another image Chapter 14: Image Generation using GAN Introduction to GAN Generating images using a DCGAN Chapter 15: Image Translation Colorizing B&W images
