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.
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
- Activation Functions
- Forward Propagation
- 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)
Regularization & Hyperparameter Tuning
Regularization techniques reduce overfitting, while hyperparameter tuning improves model performance and training efficiency.
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 using TensorFlow
- Image Classification
CNN Architectures
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
- Bidirectional RNNs
- Long Short-Term Memory (LSTM)
- Bidirectional Long Short-Term Memory (Bi-LSTM)
- Gated Recurrent Units (GRU)
Advanced Deep Learning Techniques
Advanced deep learning techniques use specialized models and architectures to solve complex AI tasks beyond traditional neural networks.
- Autoencoders
- Types of Autoencoders
- Generative Adversarial Network (GAN)
- Diffusion Models
- Encoder Decoder Models
- Seq2Seq Model
- Attention Mechanism
- Transformers
- Transfer Learning
Deep Reinforcement Learning
Deep Reinforcement Learning combines deep learning with Reinforcement Learning to enable agents to learn optimal actions through interaction with an environment.
- Introduction
- Reinforcement Learning
- Markov Decision Processes
- Deep Q-Networks (DQN)
- Policy Gradient
- REINFORCE
- Actor-Critic Methods
- Proximal Policy Optimization (PPO)
Practice
Practice deep learning concepts through projects and interview questions to strengthen your understanding.