Data Without Labels Master unsupervised learning with deep learning and generative AI 1st Edition by Vaibhav Verdhan ISBN 1617298727 9781617298721

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ISBN 10: 1617298727 
ISBN 13: 9781617298721
Author: Vaibhav Verdhan

Data Without Labels teaches you to apply a full spectrum of machine learning algorithms to raw data. You’ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You’ll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more—and you’ll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you’ll find quizzes, practice datasets, and links to research papers to help you lock in what you’ve learned and expand your knowledge.

Data Without Labels Master unsupervised learning with deep learning and generative AI 1st Table of contents:

Part I – Introduction to Unsupervised Learning
Introduction: The Power of Data Without Labels

Overview of unsupervised learning

The significance of unlabeled data in the modern AI landscape

The intersection of deep learning, generative AI, and unsupervised learning

From Supervised to Unsupervised: A Paradigm Shift

Differences between supervised and unsupervised learning

The need for unsupervised learning in real-world applications

Key Concepts in Unsupervised Learning

Clustering, dimensionality reduction, anomaly detection, and more

Exploring the importance of feature learning and representation

Part II – Foundations of Deep Learning in Unsupervised Learning
Neural Networks and Deep Learning for Unsupervised Learning

Basics of neural networks and how they relate to unsupervised tasks

Autoencoders, unsupervised pre-training, and self-supervised learning

Self-Organizing Maps and k-Means Clustering

A deeper dive into unsupervised clustering algorithms

Practical examples of SOMs and k-means in data exploration

Dimensionality Reduction Techniques

PCA (Principal Component Analysis) vs. t-SNE (t-Distributed Stochastic Neighbor Embedding)

Latent variable models and their applications in deep learning

Part III – Generative AI and Unsupervised Learning
Introduction to Generative AI

What is generative AI, and how does it differ from traditional machine learning?

Overview of key generative models: GANs, VAEs, and flow-based models

Generative Adversarial Networks (GANs) for Unsupervised Learning

Architecture and mechanics of GANs

Using GANs for data generation, image synthesis, and unsupervised learning tasks

Variational Autoencoders (VAEs) and Their Role in Unsupervised Learning

The theory behind VAEs and their application in generating complex data distributions

Latent space exploration and anomaly detection with VAEs

Flow-Based Models: Normalizing Flows and Their Applications

Understanding normalizing flows for learning complex distributions

Applications of flow-based models in unsupervised learning

Part IV – Advanced Techniques in Unsupervised Deep Learning
Clustering with Deep Learning

Deep clustering algorithms: Deep k-means, DBSCAN with deep learning

The role of deep learning in improving traditional clustering techniques

Self-Supervised Learning: Learning Representations from Unlabeled Data

Introduction to self-supervised learning concepts

How self-supervised learning enables models to learn useful features from unlabeled data

Unsupervised Reinforcement Learning: Learning from Experience

Fundamentals of reinforcement learning in the unsupervised context

Using unsupervised methods in exploration-based tasks

Part V – Applications of Unsupervised Learning and Generative AI
Anomaly Detection: Identifying Outliers in Unlabeled Data

Techniques and applications of anomaly detection in industry

Using deep learning for robust anomaly detection

Natural Language Processing (NLP) with Unsupervised Learning

Unsupervised methods in text analysis and language models

Topic modeling, word embeddings, and language generation with generative AI

Unsupervised Learning in Computer Vision

Image clustering, segmentation, and feature extraction

Generative AI in image generation and image-to-image translation

Audio and Speech Processing with Unsupervised Learning

Applications of unsupervised learning in audio signal processing

Deep learning methods for speech recognition and music generation

Part VI – Challenges and Future Directions
Challenges in Unsupervised Learning

Lack of labeled data, interpretability, and model evaluation

Dealing with the curse of dimensionality and high variance

Ethical Considerations in Unsupervised Learning and Generative AI

Bias in generative models

Ethical concerns in AI-generated content, privacy, and fairness

The Future of Unsupervised Learning: Trends and Innovations

Emerging research in unsupervised learning and generative AI

Prospects for advancing unsupervised techniques in real-world applications

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Tags: Vaibhav Verdhan, Data, generative