Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python

Learn TensorFlow 2.0
Chapter 1: TensorFlow 2.0 - An Introduction Chapter Goal: Introducing TensorFlow, major features, version 2.0 release.
Chapter 2: Supervised Learning with TensorFlow 2.0 Chapter Goal: Implementation of linear, logistic, SVM (Support Vector Machines) and random forest using TensorFlow.
Chapter 3: Neural Networks and Deep Learning with TensorFlow 2.0 Chapter Goal: Introduction to neural networks, deep learning and implementation using TensorFlow This chapter offers a detailed view of building Deep Learning models for various applications such as Forecasting using TensorFlow 2.0. The chapter also introduces optimization approaches and the techniques for hyper parameter tuning.
Chapter 4: Images with TensorFlow 2.0 Chapter Goal: TensorFlow 2.0 for images. This chapter focuses on building deep learning based models for image classification using TensorFlow 2.0. It covers advanced techniques such as GANs and transfer learning to image processing and classifications
Chapter 5: Sequence to Sequence Modeling with TensorFlow 2.0
Chapter Goal: To understand sequence modeling using TensorFlow 2.0. This chapter covers the process of using different neural networks for NLP based tasks in TensorFlow 2.0. This includes sequence to sequence prediction, text translation using deep learning in TensorFlow 2.0
Chapter 6: TensorFlow 2.0 Models in Productionization
Chapter Goal: Implementation of distributed training using TensorFlow. This chapter covers the process of scaling up the machine learning model training by implementing distributed training of TensorFlow models and deploying those models into production using TensorFlow serving layer