As we previously pointed out in the LSTM architecture, it is fed the memory and activation values from the previous timestep separately. This is distinctly separate from the assumption we made with the GRU unit, where at = ct. This dual manner of data processing is what lets us conserve relevant representations in memory across very long sequences, potentially even 1,000 timesteps! The activations are, however, always functionally related to the memory (ct) at each time step. So, we can compute the activations at a given timestep by first applying a tanh function to the memory (ct), then performing an element-wise computation of the result with the output gate value (Γo). Note that we do not initialize a weight matrix at this step, but simply apply tanh to each element in the (ct) vector. This can be mathematically represented as follows...
-
Book Overview & Buying
-
Table Of Contents
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
