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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