In the MNIST example, we used the Softmax activation function as our last layer. You may recall that the layer generated an array of 10 probability scores, adding up to 1 for a given input. Each of those 10 scores referred to the likelihood of the image being presented to our network corresponding to one of the output classes (that is, it is 90% sure it sees a 1, and 10% sure it sees a 7, for example). This approach made sense for a classification task with 10 categories. In our sentiment analysis problem, we chose a sigmoid activation function, because we are dealing with binary categories. Using the sigmoid here simply forces our network to output a prediction between 0 and 1 for any given instance of data. Hence, a value closer to 1 means that our network believes that the given piece of information is more likely to be a positive review, whereas...
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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