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1. Introduction to Neural Networks
- Overview of artificial intelligence and machine learning
- Biological inspiration: Comparison between biological and artificial neurons
- History and evolution of neural networks
- Applications of neural networks in various domains (e.g., healthcare, finance, robotics)
2. Basics of Artificial Neural Networks
- Structure of a neuron: Input, weights, bias, activation function
- Types of neural networks: Feedforward, recurrent, convolutional
- Learning paradigms: Supervised, unsupervised, reinforcement learning
- Network architectures: Single-layer, multi-layer, deep neural networks
3. Mathematical Foundations
- Linear algebra for neural networks
- Probability and statistics basics
- Gradient descent and optimization techniques
- Loss functions: Mean squared error, cross-entropy, hinge loss
- Activation functions: Sigmoid, ReLU, Tanh, Softmax
4. Feedforward Neural Networks (FNNs)
- Architecture of FNNs
- Forward propagation
- Backpropagation algorithm: Derivation and implementation
- Weight initialization techniques
- Regularization methods: L1/L2 regularization, dropout
5. Training Neural Networks
- Dataset preparation and preprocessing
- Splitting data: Training, validation, and testing
- Batch vs. stochastic vs. mini-batch gradient descent
- Learning rate and its impact
- Hyperparameter tuning: Grid search, random search
- Overfitting and underfitting
6. Advanced Neural Network Architectures
- Convolutional Neural Networks (CNNs)
- Convolutional layers, pooling layers, fully connected layers
- Applications: Image recognition, object detection
- Recurrent Neural Networks (RNNs)
- Sequence modeling, vanishing gradient problem
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
- Generative Adversarial Networks (GANs)
- Generator and discriminator networks
- Applications: Image generation, style transfer
- Autoencoders
- Encoding and decoding
- Variational autoencoders (VAEs)
7. Deep Learning Frameworks
- Introduction to TensorFlow and PyTorch
- Keras for building and training models
- Model deployment and evaluation
- Use of GPU/TPU for accelerating training
8. Neural Networks and Optimization
- Optimization algorithms: Adam, RMSprop, Adagrad
- Learning rate scheduling
- Advanced regularization techniques
- Handling imbalanced datasets
9. Applications of Neural Networks
- Computer vision: Image classification, object detection
- Natural language processing: Sentiment analysis, machine translation
- Speech recognition and synthesis
- Time series prediction
- Recommender systems
- Autonomous vehicles and robotics
10. Challenges in Neural Networks
- Vanishing and exploding gradients
- High computational cost and memory requirements
- Lack of interpretability and explainability
- Ethical and societal implications
11. Case Studies and Projects
- Building a digit recognizer using MNIST dataset
- Sentiment analysis using RNNs
- Image classification using CNNs
- Time series forecasting with LSTMs
- GANs for image generation
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