Artificial Neural Networks (ANN) B.Tech Handwritten Notes For University Exams

Here’s a comprehensive syllabus for Artificial Neural Networks (ANNs), typically part of computer science, machine learning, or AI programs:

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