Overview

1. Understanding Neural Networks

• Introduction to Neural Networks
  • What are neural networks?
  • Basic components and architecture
  • Types of neural networks (Feedforward, Convolutional, Recurrent, etc.)
• Biological Inspiration
  • How neural networks are inspired by the human brain
  • Similarities and differences

2. Neurons: The Building Blocks

• Structure of a Artificial Neuron
  • Inputs, Weights, Biases, Activation Functions
• Activation Functions
  • Role of activation functions
  • Common types: Sigmoid, ReLU, Tanh, Softmax, etc.
• Learning Process
  • How neurons learn (weight adjustments)
  • Gradient Descent and backpropagation (coming soon)

3. Designing The Architecture

• Layers of a Neural Network
  • Input layer, Hidden layers, Output layer
• Types of Layers
  • Fully connected (Dense) layers
  • Convolutional layers (for CNNs)
  • Recurrent layers (for RNNs)
• Choosing the Number of Layers and Neurons
  • How to decide the depth and width of your network

I have also layed out the workflow for how to design a model to fit a certain task/dataset in Network Modeling Workflow

4. Loss Functions and Optimization

• Loss Functions
  • Purpose and types: MSE, Cross-Entropy, Hinge, Huber
• Optimization Algorithms
  • Gradient Descent (SGD, Mini-batch, etc.)
  • Advanced optimizers: Adam, RMSprop, Adagrad
• Regularization Techniques
  • Preventing overfitting: L1/L2 regularization, Dropout, etc.

5. Training The Neural Network

• Forward Propagation
  • How inputs are processed through the network
• Backward Propagation
  • Calculating gradients and updating weights
• Training Cycles
  • Epochs, Batches, and Iterations
• Evaluation Metrics
  • Accuracy, Precision, Recall, F1 Score, etc.

6. Data Preparation

• Dataset Collection
  • Finding and curating datasets
• Data Preprocessing
  • Normalization, Standardization, Handling missing data
• Data Augmentation
  • Techniques to artificially increase dataset size (especially in image processing)

7. Model Evaluation and Tuning

• Cross Validation
  • Ensuring generalization with techniques like k-fold cross-validation
• Hyperparameter Tuning
  • Tuning learning rate, batch size, number of epochs, etc.
  • Grid Search, Random Search, Bayesian Optimization
• Generative Models
  • Generative Adversarial Network, Variational Autoencoder, and other generative approaches
• Neural Network Interpretability