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Deep Learning and AI Superhero
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Deep Learning and AI Superhero

by Cuantum Technologies

Data Science

Book Details

Book Title

Deep Learning and AI Superhero

Author

Cuantum Technologies

Publisher

Packt Publishing

Publication Date

2025

ISBN

9798895873595

Number of Pages

1160

Language

English

Format

PDF

File Size

9MB

Subject

Deep Learning

Table of Contents

  • Who we are
  • Our Philosophy:
  • Our Expertise:
  • Code Blocks Resource
  • Premium Customer Support
  • TABLE OF CONTENTS
  • Introduction
  • Part 1: Neural Networks and Deep Learning Basics
  • Chapter 1: Introduction to Neural Networks and Deep Learning
  • 1.1 Perceptron and Multi-Layer Perceptron (MLP)
  • 1.2 Backpropagation, Gradient Descent, and Optimizers
  • 1.3 Overfitting, Underfitting, and Regularization Techniques
  • 1.4 Loss Functions in Deep Learning
  • Practical Exercises Chapter 1
  • Chapter 1 Summary
  • Chapter 2: Deep Learning with TensorFlow 2.x
  • 2.1 Introduction to TensorFlow 2.x
  • 2.2 Building, Training, and Fine-Tuning Neural Networks in TensorFlow
  • 2.3 Using TensorFlow Hub and Model Zoo for Pretrained Models
  • 2.4 Saving, Loading, and Deploying TensorFlow Models
  • Practical Exercises Chapter 2
  • Chapter 2 Summary
  • Chapter 3: Deep Learning with Keras
  • 3.1 Introduction to Keras API in TensorFlow 2.x
  • 3.2 Building Sequential and Functional Models with Keras
  • 3.3 Model Checkpointing, Early Stopping, and Callbacks in Keras
  • 3.4 Deploying Keras Models to Production
  • Practical Exercises Chapter 3
  • Chapter 3 Summary
  • Quiz Part 1: Neural Networks and Deep Learning Basics
  • Answers to the Quiz:
  • Part 4: Advanced Deep Learning Frameworks
  • Chapter 4: Deep Learning with PyTorch
  • 4.1 Introduction to PyTorch and its Dynamic Computation Graph
  • 4.2 Building and Training Neural Networks with PyTorch
  • 4.3 Transfer Learning and Fine-Tuning Pretrained PyTorch Models
  • 4.4 Saving and Loading Models in PyTorch
  • 4.5 Deploying PyTorch Models with TorchServe
  • Practical Exercises Chapter 4
  • Chapter 4 Summary
  • Chapter 5: Convolutional Neural Networks (CNNs)
  • 5.1 Introduction to CNNs and Image Processing
  • 5.2 Implementing CNNs with TensorFlow, Keras, and PyTorch
  • 5.3 Advanced CNN Techniques (ResNet, Inception, DenseNet)
  • 5.4 Practical Applications of CNNs (Image Classification, Object Detection)
  • Practical Exercises Chapter 5
  • Chapter 5 Summary
  • Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs
  • 6.1 Introduction to RNNs, LSTMs, and GRUs
  • 6.2 Implementing RNNs and LSTMs in TensorFlow, Keras, and PyTorch
  • 6.3 Applications of RNNs in Natural Language Processing
  • 6.4 Transformer Networks for Sequence Modeling
  • Practical Exercises Chapter 6
  • Chapter 6 Summary
  • Quiz Part 2: Advanced Deep Learning Frameworks
  • Answers:
  • Part 5: Cutting-Edge AI and Practical Applications
  • Chapter 7: Advanced Deep Learning Concepts
  • 7.1 Autoencoders and Variational Autoencoders (VAEs)
  • 7.2 Generative Adversarial Networks (GANs) and Their Applications
  • 7.3 Transfer Learning and Fine-Tuning Pretrained Networks
  • 7.4 Self-Supervised Learning and Foundation Models
  • Practical Exercises Chapter 7
  • Summary Chapter 7
  • Chapter 8: Machine Learning in the Cloud and Edge Computing
  • 8.1 Running Machine Learning Models in the Cloud (AWS, Google Cloud, Azure)
  • 8.2 Introduction to TensorFlow Lite and ONNX for Edge Devices
  • 8.3 Deploying Models to Mobile and Edge Devices
  • Practical Exercises Chapter 8
  • Summary Chapter 8
  • Chapter 9: Practical Machine Learning Projects
  • 9.1 Project 1: Predicting House Prices with Regression
  • 9.2 Project 2: Sentiment Analysis Using Transformer-based Models
  • 9.3 Project 3: Image Classification with CNNs
  • 9.4 Project 4: Time Series Forecasting with LSTMs (Improved)
  • 9.5 Project 5: GAN-based Image Generation
  • Quiz Part 3: Cutting-Edge AI and Practical Applications
  • Answers
  • Conclusion
  • Where to continue?
  • Know more about us