In the last chapter, we discussed how humans tend to process events in a sequential manner. We break down our daily tasks into a sequence of smaller actions, without giving it much thought. When you get up in the morning, you may choose to visit the bathroom before making yourself breakfast. In the bathroom, you may choose to shower first before brushing your teeth. Some may choose to execute both tasks simultaneously. Often, these choices boil down to our individual preferences and time restrictions. From another perspective, a lot of how we go about doing the things we do has to do with how our brain has chosen to represent the importance of these relative tasks, governed by information it has saved about the near and distant past. For example, when you wake up in the morning, you may be inclined to shower first if you live in an apartment block...
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
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
Customer Reviews
