- No; the generator and discriminator are functionally different.
- No, it can't, because the output of a discriminator must be a probability (that is, pi ∈ (0, 1)).
- Yes; it's correct. The discriminator can learn to output different probabilities very quickly, and the gradients of its loss function can become close to 0, reducing the magnitude of the correction feedback provided to the generator.
- Yes; it's normally quite slower.
- The critic is slower, because the variables are clipped after every update.
- As the supports are disjointed, the Jensen-Shannon divergence is equal to log(2).
- The goal is to develop highly selective units whose responses are only elicited by a specific feature set.
- It's impossible to know the final organization during the early stages of the training process; therefore, it's not a good practice to force the premature...
Hands-On Unsupervised Learning with Python
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Hands-On Unsupervised Learning with Python
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Overview of this book
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
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Current Title:
Hands-On Unsupervised Learning with Python
Table of Contents (12 chapters)
Preface
Free Chapter
Getting Started with Unsupervised Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering in Action
Soft Clustering and Gaussian Mixture Models
Anomaly Detection
Dimensionality Reduction and Component Analysis
Unsupervised Neural Network Models
Generative Adversarial Networks and SOMs
Assessments
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