Deep Learning Topics
1. Neural Networks Basics
Perceptrons
Activation Functions (ReLU, Sigmoid, Tanh)
Loss Functions (MSE, Cross-Entropy)
Backpropagation and Gradient Descent
2. Deep Neural Networks (DNNs)
Multi-layer neural networks
Vanishing and exploding gradient problems
Weight initialization techniques
Batch normalization and dropout
3. Convolutional Neural Networks (CNNs)
Convolution, pooling, padding
Image classification and object detection
Architectures: LeNet, AlexNet, VGG, ResNet, EfficientNet
Transfer learning and fine-tuning
4. Recurrent Neural Networks (RNNs)
Sequence modeling
Vanishing gradient problem in RNNs
LSTM (Long Short-Term Memory)
GRU (Gated Recurrent Unit)
Applications: Time series, language modeling
5. Transformers and Attention Mechanisms
Self-attention and multi-head attention
Encoder-decoder architectures
Positional encoding
Models: BERT, GPT, T5, Vision Transformers (ViT)
Applications in NLP, vision, and audio
6. Generative Models
Autoencoders (AE, Variational Autoencoders – VAE)
Generative Adversarial Networks (GANs)
DCGAN, CycleGAN, StyleGAN
Diffusion models
Applications: Image generation, data augmentation
7. Reinforcement Learning (with Deep Learning)
Deep Q-Networks (DQN)
Policy gradients, Actor-Critic methods
Applications in games, robotics, and optimization
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