Deep Learning Tutorial

Deep Learning is a branch of Artificial Intelligence (AI) that enables machines to learn patterns from large amounts of data using multi-layered neural networks. It is widely used in image recognition, speech processing and natural language understanding.

Basics

Neural networks are the basis of deep learning, using interconnected layers of neurons to learn patterns from data.

• Neural Networks
• Biological Neurons vs Artificial Neurons
• Single Layer Perceptron
• Multi-Layer Perceptron
• Artificial Neural Networks (ANNs)
• Types of Neural Networks
• Architecture and Learning process in neural network
• Deep Learning Framework

Components of Neural Networks

Neural networks have key components that control data flow and learning, helping the model adjust parameters and improve predictions.

• Layers
• Weights and Biases
• Forward Propagation
• Activation Functions
• Loss Functions
• Backpropagation
• Learning Rate

Optimization Algorithm

Optimization algorithms update weights and biases to minimize loss, helping the model converge faster and perform better.

• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-batch Gradient Descent
• Momentum Optimizer
• Adagrad Optimizer
• RMSProp Optimizer
• Adam (Adaptive Moment Estimation)

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are deep learning models designed for image data that automatically detect features like edges, textures and shapes.

• Introduction
• Padding
• Convolutional Layers
• Pooling Layers
• Fully Connected Layers
• Backpropagation in CNNs
• Building CNN using PyTorch
• Building CNN in Tensorflow
• Image Classification

CNN Architectures

• LeNet-5
• AlexNet
• VGGnet
• VGG-16
• GoogLeNet/Inception
• ResNet (Residual Network)
• MobileNet
• U-Net

Recurrent Neural Networks

RNNs are a class of neural networks that are used for modeling sequence data such as time series or natural language.

• Introduction
• How RNN Differs from Feedforward Neural Networks
• Backpropagation Through Time (BPTT)
• Vanishing Gradient Problem
• Training of RNN Model
• Sentiment Analysis with RNN
• Bidirectional RNNs
• Long Short-Term Memory (LSTM)
• Bidirectional Long Short-Term Memory (Bi-LSTM)
• Gated Recurrent Units (GRU)

Generative Models

Generative models create new data similar to training data and are used in image generation and other creative applications.

• Introduction
• Autoencoders
• Deep Convolutional GAN (DCGAN)
• Conditional GAN (cGAN)
• Cycle-Consistent GAN (CycleGAN)
• Super-Resolution GAN (SRGAN)
• StyleGAN
• Types of Autoencoders

Deep Reinforcement Learning

Deep Reinforcement Learning combines deep learning with Reinforcement Learning to enable agents to learn optimal actions through interaction with an environment.

• Introdcution
• Reinforcement Learning
• Markov Decision Processes
• Deep Q-Networks (DQN)
• REINFORCE
• Actor-Critic Methods
• Proximal Policy Optimization (PPO)

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