The point behind peephole connections is the need to capture the information of time lags. In other words, we wish to include the information conveyed by time intervals between sub-patterns of sequences in our modeling efforts. This is relevant not only for certain language processing tasks (such as speech recognition), but also for numerous other tasks ranging from machine motor control to maintaining elaborate rhythms in computer-generated music. Previous approaches to tasks such as speech recognition employed the use of Hidden Markov Models (HMMs). These are essentially statistical models that estimate the probability of a set of observations based on the sequence of hidden state transitions. In the case of speech processing, observations are defined as segments of digital signals corresponding to speech, while Markov hidden states are the...
-
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
