Chapter 1: Introduction to Neural Networks
- Lesson 1: What are Neural Networks?
- Lesson 2: Biological Inspiration vs Artificial Neural Networks
- Lesson 3: Historical Development of Neural Networks
- Lesson 4: Overview of Course Structure and Objectives
- Lesson 5: Applications of Neural Networks in Real Life
Chapter 2: Fundamentals of Neural Network Design
- Lesson 1: Anatomy of a Neural Network (Neuron, Layers, Weights, Biases)
- Lesson 2: Activation Functions and Their Role
- Lesson 3: Feedforward Neural Networks
- Lesson 4: Forward Propagation in Detail
- Lesson 5: Representation of Neural Networks (Graphical and Mathematical)
Chapter 3: Training Neural Networks
- Lesson 1: The Concept of Learning in Neural Networks
- Lesson 2: Loss Functions (Mean Squared Error, Cross-Entropy, etc.)
- Lesson 3: Gradient Descent Algorithm
- Lesson 4: Backpropagation Explained
- Lesson 5: Challenges in Training (Overfitting, Underfitting)
Chapter 4: Supervised Learning with Neural Networks
- Lesson 1: Introduction to Supervised Learning
- Lesson 2: Training with Labeled Data
- Lesson 3: Regression and Classification Tasks
- Lesson 4: Examples and Use Cases
- Lesson 5: Evaluation Metrics (Accuracy, Precision, Recall)
Chapter 5: Optimization Techniques
- Lesson 1: Stochastic Gradient Descent (SGD)
- Lesson 2: Momentum and Nesterov Accelerated Gradient
- Lesson 3: Adaptive Learning Rate Methods (Adagrad, RMSProp)
- Lesson 4: Adam Optimizer in Detail
- Lesson 5: Choosing the Right Optimization Method
Chapter 6: Data Preparation and Feature Engineering
- Lesson 1: Importance of Data Quality
- Lesson 2: Normalization and Standardization
- Lesson 3: Handling Missing Data and Outliers
- Lesson 4: Feature Selection and Feature Scaling
- Lesson 5: Augmenting Data for Neural Networks
Chapter 7: Types of Neural Networks
- Lesson 1: Introduction to Different Architectures
- Lesson 2: Single-Layer Neural Networks
- Lesson 3: Multi-Layer Perceptrons (MLPs)
- Lesson 4: Radial Basis Function Networks
- Lesson 5: Key Differences Between Architectures
Chapter 8: Regularization Techniques
- Lesson 1: Importance of Regularization
- Lesson 2: L1 and L2 Regularization
- Lesson 3: Dropout Explained
- Lesson 4: Batch Normalization
- Lesson 5: Techniques to Prevent Overfitting
Chapter 9: Introduction to Deep Learning
- Lesson 1: What is Deep Learning?
- Lesson 2: The Depth of Neural Networks
- Lesson 3: Importance of Nonlinearity
- Lesson 4: Key Differences Between Neural Networks and Deep Learning
- Lesson 5: Applications and Current Trends
Chapter 10: Practical Implementation with Tools
- Lesson 1: Introduction to Deep Learning Libraries (TensorFlow, PyTorch, Keras)
- Lesson 2: Setting Up the Environment
- Lesson 3: Writing Your First Neural Network Code
- Lesson 4: Visualizing Training with TensorBoard
- Lesson 5: Debugging Neural Networks
Chapter 11: Advanced Neural Network Concepts
- Lesson 1: Perceptron Learning Rule
- Lesson 2: Linear Transformations for Neural Networks
- Lesson 3: Supervised Hebbian Learning
- Lesson 4: Widrow-Hoff Learning
Chapter 12: Variations and Specialized Learning Techniques
- Lesson 1: Variations on Backpropagation
- Lesson 2: Dynamic Networks
- Lesson 3: Associative Learning
Chapter 13: Competitive and Specialized Networks
- Lesson 1: Competitive Networks
- Lesson 2: Grossberg Network
- Lesson 3: Adaptive Resonance Theory
- Lesson 4: Hopfield Network
Chapter 1: Advanced Optimization Techniques
- Lesson 1: Gradient Descent Variants
- Lesson 2: Optimization Challenges in Deep Learning
- Lesson 3: Batch Normalization and Weight Initialization
- Lesson 4: Adaptive Optimization Algorithms (Adam, RMSProp)
- Lesson 5: Regularization Techniques
Chapter 2: Convolutional Neural Networks (CNNs)
- Lesson 1: CNN Architecture and Operations
- Lesson 2: Feature Maps and Filters
- Lesson 3: Transfer Learning with CNNs
- Lesson 4: Advanced Architectures (ResNet, DenseNet)
- Lesson 5: Applications in Image Processing
Chapter 3: Recurrent Neural Networks (RNNs)
- Lesson 1: Basics of Recurrent Networks
- Lesson 2: Long Short-Term Memory (LSTM)
- Lesson 3: Gated Recurrent Units (GRUs)
- Lesson 4: Advanced RNN Applications
- Lesson 5: Challenges in Training RNNs
Chapter 4: Transformers and Attention Mechanisms
- Lesson 1: Attention Mechanism Fundamentals
- Lesson 2: Transformer Architecture
- Lesson 3: Multi-Head Attention
- Lesson 4: Applications of Transformers
- Lesson 5: Vision Transformers (ViT)
Chapter 5: Autoencoders and Variational Autoencoders
- Lesson 1: Basics of Autoencoders
- Lesson 2: Variational Autoencoders (VAEs)
- Lesson 3: Applications in Data Compression
- Lesson 4: Anomaly Detection with Autoencoders
- Lesson 5: Advanced Autoencoder Techniques
Chapter 6: Deep Belief Networks and Deep Boltzmann Machines
- Lesson 1: Overview of Probabilistic Neural Networks
- Lesson 2: Deep Belief Networks: Detailed Architecture and Training
- Lesson 3: Deep Boltzmann Machines: Concepts and Training
- Lesson 4: Applications of DBNs and DBMs
- Lesson 5: Comparisons and Use Cases
Chapter 7: Generative Models
- Lesson 1: Introduction to Generative Models
- Lesson 2: Generative Adversarial Networks (GANs)
- Lesson 3: Applications of GANs
- Lesson 4: Challenges in Training GANs
- Lesson 5: Variants of GANs (CycleGAN, StyleGAN)
Chapter 8: Advanced Sequence Models
- Lesson 1: Sequence-to-Sequence Models
- Lesson 2: Attention-Based Models
- Lesson 3: Bidirectional RNNs
- Lesson 4: Applications in Sequence Modeling
- Lesson 5: Challenges and Solutions
Chapter 9: Self-Supervised and Semi-Supervised Learning
- Lesson 1: Fundamentals of Self-Supervised Learning
- Lesson 2: Pretext Tasks in Self-Supervised Learning
- Lesson 3: Semi-Supervised Learning Techniques
- Lesson 4: Applications and Case Studies
- Lesson 5: Future Directions in Self-Supervised Learning
Chapter 10 Few-Shot and Zero-Shot Learning
- Lesson 1: Introduction to Few-Shot Learning
- Lesson 2: Introduction to Zero-Shot Learning
- Lesson 3: Techniques for Few-Shot Learning
- Lesson 4: Zero-Shot Learning Applications
- Lesson 5: Challenges and Future Trends
Chapter 11: Neural Network Interpretability
- Lesson 1: Importance of Interpretability
- Lesson 2: Visualization Techniques
- Lesson 3: Explainable AI (XAI) Methods
- Lesson 4: Case Studies in Model Interpretability
- Lesson 5: Ethical Considerations
Chapter 12: Advanced Regularization Techniques
- Lesson 1: Dropout and DropConnect
- Lesson 2: Data Augmentation Strategies
- Lesson 3: Weight Pruning and Sparsity
- Lesson 4: Ensemble Learning with Neural Networks
- Lesson 5: Case Studies
Chapter 13: Reinforcement Learning with Neural Networks
- Lesson 1: Basics of Reinforcement Learning
- Lesson 2: Policy Gradient Methods
- Lesson 3: Deep Q-Learning
- Lesson 4: Actor-Critic Methods
- Lesson 5: Applications of Reinforcement Learning
Chapter 14: Neural Architecture Search (NAS)
- Lesson 1: Introduction to NAS
- Lesson 2: Techniques for Neural Architecture Search
- Lesson 3: Applications of NAS
- Lesson 4: Challenges in NAS
- Lesson 5: Future Trends in NAS
Chapter 15: Distributed and Parallel Training
- Lesson 1: Distributed Training Strategies
- Lesson 2: Model Parallelism vs Data Parallelism
- Lesson 3: Frameworks for Distributed Training
- Lesson 4: Challenges in Large-Scale Training
- Lesson 5: Case Studies
Chapter 16: Advanced Topics in Transformers
- Lesson 1: Transformer Variants
- Lesson 2: Large Language Models (LLMs)
- Lesson 3: Fine-Tuning Techniques
- Lesson 4: Challenges in Scaling Transformers
- Lesson 5: Applications in Multiple Domains
Chapter 17: Emerging Trends in Neural Networks
- Lesson 1: Neural Tangent Kernel (NTK)
- Lesson 2: Liquid Neural Networks
- Lesson 3: Implicit Neural Representations
- Lesson 4: Spiking Neural Networks
- Lesson 5: Future Directions
Chapter 18: Ethics and Bias in Deep Learning
- Lesson 1: Identifying Bias in Models
- Lesson 2: Mitigating Bias in Neural Networks
- Lesson 3: Ethical AI Development
- Lesson 4: Regulatory Frameworks
- Lesson 5: Case Studies in Ethical AI
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