How can we ensure that our agent relies on a good balance of old and new strategies? This problem is made worse through the random initialization of weights for our Q-network. Since the predicted Q-values are a result of these random weights, the model will generate sub-optimal predictions at the initial training epochs, which in turn results in poor Q-value learning. Naturally, we don't want our network to rely too much on strategies it generates at first for given state-action pairs. Just like the dopamine addicted rat, the agent cannot be expected to perform well in the long term if it doesn't explore new strategies and expand its horizons instead of exploiting known strategies. To address this problem, we must implement a mechanism that encourages the agent to try out new actions, ignoring the learned Q-values. Doing so basically...
Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras
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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.
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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
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