Deep Learning: Syllabus

Mastering Deep Learning: From Fundamentals to Advanced Applications with Python

Deep Learning has revolutionized artificial intelligence by enabling breakthroughs in computer vision, natural language processing, and reinforcement learning. This book takes a practical approach to deep learning, covering neural networks, optimization, model architectures, and deployment strategies. By the end of this book, readers will have hands-on experience implementing deep learning models with Python, TensorFlow, and PyTorch.

Module 1: Introduction to Deep Learning#

• What is deep learning? Evolution and real-world applications
• Comparison between traditional machine learning and deep learning
• Setting up Python environment for deep learning (TensorFlow, PyTorch, Keras)
• Understanding the deep learning workflow

Module 2: Foundations of Artificial Neural Networks (ANNs)#

• Biological vs. Artificial Neurons
• Understanding Perceptrons and Multilayer Perceptrons (MLPs)
• Activation functions (ReLU, Sigmoid, Tanh, Softmax)
• Implementing a simple ANN from scratch in Python

Module 3: Optimization Techniques for Neural Networks#

• Loss functions (MSE, Cross-Entropy, Huber Loss)
• Gradient Descent, Adam, RMSprop, and other optimizers
• Vanishing and Exploding Gradient Problems
• Batch Normalization, Dropout, and Regularization techniques

Module 4: Convolutional Neural Networks (CNNs) for Image Processing#

• Convolutional layers, pooling, and feature extraction
• Building CNNs with TensorFlow and PyTorch
• Transfer Learning using pre-trained models (VGG16, ResNet, EfficientNet)
• Data Augmentation and improving model generalization

Module 5: Recurrent Neural Networks (RNNs) and Sequence Modeling#

• Understanding sequential data and RNN architecture
• Implementing RNNs with TensorFlow and PyTorch
• Long Short-Term Memory (LSTMs) and Gated Recurrent Units (GRUs)
• Real-world applications: Text generation, speech recognition, time-series forecasting

Module 6: Attention Mechanisms and Transformers#

• Understanding Self-Attention and Multi-Head Attention
• Transformer architecture (BERT, GPT, T5)
• Implementing transformers for text processing
• Fine-tuning transformer models for NLP tasks

Module 7: Generative Adversarial Networks (GANs)#

• Introduction to generative models
• Understanding Generator and Discriminator networks
• Implementing GANs with PyTorch and TensorFlow
• Applications: Image generation, style transfer, deepfake detection

Module 8: Reinforcement Learning (RL) and Deep Q-Networks#

• Basics of Reinforcement Learning (RL)
• Implementing Q-learning and Deep Q-Networks (DQN)
• Policy Gradient Methods and Actor-Critic Models
• Real-world applications of RL in robotics and gaming

Module 9: Hyperparameter Tuning and Model Optimization#

• Grid Search, Random Search, and Bayesian Optimization
• Fine-tuning deep learning models
• Using TensorBoard for monitoring training performance
• Avoiding overfitting and underfitting in deep learning models

Module 10: Model Deployment and Productionization#

• Deploying deep learning models with Flask and FastAPI
• Using TensorFlow Serving and TorchScript for efficient inference
• Containerizing models with Docker and Kubernetes
• Implementing CI/CD pipelines for model deployment

Module 11: Deep Learning for Edge AI and Mobile Applications#

• Running deep learning models on mobile devices with TensorFlow Lite
• Optimizing models for edge devices (NVIDIA Jetson, Raspberry Pi)
• Using ONNX for cross-platform model deployment
• Real-time AI applications in IoT and edge computing

Module 12: Ethical AI and Explainable Deep Learning#

• Understanding AI bias and fairness
• Explainability techniques (SHAP, LIME, Integrated Gradients)
• Privacy concerns and federated learning for secure AI
• Legal and ethical considerations in AI development

Hands-On Projects

Project 1: Handwritten Digit Recognition with CNNs#

• Building and training a CNN for MNIST classification
• Implementing data augmentation techniques
• Evaluating model performance with confusion matrices

Project 2: Sentiment Analysis using Transformers#

• Preprocessing text data for NLP tasks
• Fine-tuning a BERT-based sentiment analysis model
• Deploying the model as an API for real-time analysis

Project 3: Image Generation with GANs#

• Training a GAN to generate realistic images
• Fine-tuning a StyleGAN model
• Experimenting with conditional GANs for guided image generation

Project 4: Stock Market Prediction with LSTMs#

• Collecting and processing time-series financial data
• Training LSTMs for stock price forecasting
• Evaluating model performance with RMSE and MAE

Project 5: Deploying a Real-Time Deep Learning Model#

• Implementing an AI model deployment pipeline
• Optimizing inference speed with TensorFlow Lite or ONNX
• Deploying the model using FastAPI and Docker

References#

• Deep Learning with TensorFlow
• PyTorch Official Documentation
• Stanford CS231n: Convolutional Neural Networks
• Hugging Face Transformers