We saw earlier that the intermediate layers of our ConvNet seemed to encode some pretty clear detectors of face edges. It is harder to distinguish, however, whether our network understands what a smile actually is. You will notice in our smiling faces dataset that all pictures have been taken on the same background at the same approximate angle from the camera. Moreover, you will notice that the individuals in our dataset tend to smile as they lift their head up high and clear, yet mostly tilt their head downward while frowning. That's a lot of opportunity for our network to overfit on some irrelevant pattern. Hence, how do we actually know that our network understands that a smile has more to do with the movement of a person’s lips than it has to do with the angle at which someone’s face is tilted? As we saw in our neural network fails...
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
How would like to rate this book
Customer Reviews
