Deep-Learning

Deep Learning

1. Fundamentals of Deep Learning

• Mathematical Foundations
  • Scalars, vectors, matrices, and tensors
  • Operations: matrix transpose, dot product, and inversion
  • Vector spaces and their properties
• Introduction to Neural Networks
  • Perceptron: structure, components, and binary classification
  • Activation functions: Sigmoid, ReLU, tanh, and step functions
  • Basics of feedforward neural networks

2. Linear and Logistic Regression

• Linear Regression
  • Concepts: dependent and independent variables
  • Regression equation and modeling
  • Applications in prediction and data analysis
• Logistic Regression
  • Sigmoid function for binary classification
  • Logistic regression for multi-class classification
  • Practical examples: classification using the Iris dataset
  • Comparison of odds and probabilities

3. Neural Networks and Training

• Deep Neural Networks
  • Structure of DNN: input, hidden, and output layers
  • Challenges in training deep networks: vanishing and exploding gradients
• Training Techniques
  • Regularization methods: weight decay, dropout, and batch normalization
  • Early stopping as a technique to prevent overfitting
  • Optimization algorithms: Stochastic Gradient Descent (SGD), Adam, RMSProp
  • Mini-batch gradient descent and its advantages

4. Backpropagation

• Fundamentals of Backpropagation
  • Forward pass to compute output
  • Backward pass to calculate gradients using the chain rule
  • Weight updates based on computed gradients
• Applications
  • Backpropagation in feedforward neural networks
  • Backpropagation through time (BPTT) for recurrent networks
  • Examples: gradient calculation and error minimization in small networks

5. Convolutional Neural Networks (CNNs)

• Introduction to CNNs
  • Components: convolutional layers, pooling layers, and fully connected layers
  • Feature extraction using kernels and filters
• Architectures and Applications
  • Famous architectures: AlexNet, ResNet, VGG, GoogLeNet
  • Use cases: object detection, style transfer, super-resolution
• Training CNNs
  • Backpropagation for weight updates in convolutional layers
  • Shared weights and localized feature extraction

6. Recurrent Neural Networks (RNNs)

• Basics of RNNs
  • Sequential data modeling using RNNs
  • Hidden states and time-step dependencies
• Advanced RNN Models
  • Long Short-Term Memory (LSTM): structure and role of cell states
  • Gated Recurrent Unit (GRU): simplified LSTM with fewer parameters
• Training Techniques
  • Addressing vanishing gradients in RNNs
  • Backpropagation through time (BPTT) for sequence-based tasks

7. Generative Models

• Generative Adversarial Networks (GANs)
  • Architecture: generator and discriminator interaction
  • Adversarial training process
  • Applications: image synthesis, text-to-image generation, super-resolution
• Training Challenges
  • Stability issues in GAN training
  • Techniques to balance generator and discriminator improvements

8. Autoencoders

• Encoder-Decoder Architecture
  • Compression and reconstruction using autoencoders
  • Dimensionality reduction and feature extraction
• Advanced Autoencoders
  • Variational Autoencoders (VAE) for probabilistic modeling
  • Use cases in anomaly detection and generative tasks

9. Performance Evaluation

• Evaluation Metrics
  • Accuracy, precision, recall, and F1-score
  • Confusion matrix: true positive, false positive, true negative, false negative
  • ROC curves and AUC as measures of classifier performance
• Model Comparison
  • Methods for comparing predictive capabilities of models
  • Limitations of specific metrics like accuracy in imbalanced datasets

10. Advanced Topics in Deep Learning

• Transfer Learning
  • Leveraging pre-trained models for specific tasks
  • Popular models: AlexNet, ResNet, and their applications
• Deep Learning Applications
  • Fields of use: healthcare, agriculture, genomics, autonomous systems
  • Specialized tasks: video captioning, behavior prediction, and personalized recommendations

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