Note that this does not necessarily mean that the movement of all stocks, in all industries, can be better predicted through inclusion of social media data. However, it does illustrate our point that there is some room for heuristic-based feature generation that may allow additional signals to be leveraged for better predictive outcomes. To provide some closing comments on our experiments, we also notice that the simple GRU and the stacked LSTMs both have smoother predictive curves, and are less likely to be swayed by noisy input sequences. They perform remarkably well at conserving the general trend of the stock. The out-of-set accuracy of these models (assessed with the MAE between the predicted and actual value) tells us that they perform slightly worse than the feedforward network and the simple LSTM. However, we may prefer to employ the models with the smoother...
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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.
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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