Now that we have our training data preprocessed and ready in tensor format, we can try a slightly different approach than previous chapters. Normally, we would go ahead and build a single model and then proceed to train it. Instead, we will construct several models, each reflecting a different RNN architecture, and train them successively to see how each of them do at the task of generating character-level sequences. In essence, each of these models will leverage a different learning mechanism and induct its proper language model, based on sequences of characters it sees. Then, we can sample the language models that are learned by each network. In fact, we can even sample our networks in-between training epochs to see how our network is doing at generating Shakespearean phrases at the level of each epoch. Before we continue to build our networks, we...
Hands-On Neural Networks with Keras
By :
Hands-On Neural Networks with Keras
By:
Overview of this book
Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.
Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.
By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.
Table of Contents (16 chapters)
Preface
Overview of Neural Networks
A Deeper Dive into Neural Networks
Signal Processing - Data Analysis with Neural Networks
Section 2: Advanced Neural Network Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Reinforcement Learning with Deep Q-Networks
Section 3: Hybrid Model Architecture
Autoencoders
Generative Networks
Section 4: Road Ahead
Contemplating Present and Future Developments
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
