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• Our Philosophy:
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• 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?
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