TY  - BOOK
AU  - Vasilev, Ivan
TI  - Advanced Deep Learning with Python: design and implement advanced next-generation AI solutionsusing TensorFlow and PyTorch 
SN  - 9781789956177
U1  - 005.133 VAS-I 
PY  - 2019///
CY  - India
PB  - Packt Publishing
KW  - Machine learning
KW  - Neural networks (Computer science)
KW  - Python (Computer program language)
KW  - Artificial intelligence
N1  - To build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.

You’ll start by understanding the building blocks and the math behind neural networks and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.

By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

What you will learn
Cover advanced and state-of-the-art neural network architectures
Understand the theory and math behind neural networks
Train DNNs and apply them to modern deep learning problems
Use CNN's for object detection and image segmentation
Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models
Understand DL techniques, such as meta-learning and graph neural networks
ER  -