Consider a new-born human baby. At first, it is even incapable of breathing and has to be motivated to do so by a few friendly spanks, delivered by the attending physician. For the first few months, this being does not seem to do anything remarkable and is incapable of independent movement, let alone thought. Yet, slowly, this same baby develops an internal model of the world around it. It becomes better and better at distinguishing all this light it sees and the cacophony of sounds it hears. Soon, it starts recognizing things such as movement, perhaps in the guise of a friendly face, hovering around with deliciously gooey substances. A bit later, it develops a premature internal physics engine, through the observation of the world around it. It then uses these representations to first crawl, then toddle, and eventually even walk, progressively updating...
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.
Related Content you might be interested in
Current Title:
Hands-On Neural Networks with Keras
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