Deep Learning Tutorial

30 min read Machine Learning in AI

This is a free course. Log in to run the code exercises and track your progress.

Log in Enroll free

Deep Learning Tutorial

Deep Learning is an Artificial Intelligence (AI) subfield in which stacked neural networks learn rich patterns from large datasets. It powers applications such as recognizing images, processing speech, and understanding natural language.

Basics

Neural networks underpin deep learning: layers of linked units discover structure in data.

  • Neural Networks
  • Types of Neural Networks
  • Artificial Neural Networks (ANNs)
  • Deep Learning Framework

Components of Neural Networks

Core building blocks govern how information moves and how learning updates parameters so predictions get better over time.

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

Optimization Algorithm

Optimizers revise weights and biases to drive loss down, speeding convergence and improving results.

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

Regularization & Hyperparameter Tuning

Regularization curbs overfitting; tuning hyperparameters raises accuracy and training efficiency.

  • Dropout
  • Batch Normalization
  • Early Stopping
  • L1 & L2 Regularization
  • Epoch
  • Hyperparameter Tuning

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) specialize in visual data, automatically picking up edges, textures, shapes, and related cues.

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

CNN Architectures

  • LeNet-5
  • AlexNet
  • VGG-16
  • VGG-19
  • GoogLeNet/Inception
  • ResNet (Residual Network)
  • MobileNet
  • U-Net
  • ViT

Recurrent Neural Networks

RNNs model ordered data—time series, language, and other sequences—by carrying information across steps.

  • 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

More specialized architectures tackle hard AI problems that go beyond standard feedforward nets.

  • 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 merges deep networks with RL so agents discover strong actions by interacting with an environment.

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

Practice

Solidify deep learning through projects and interview prep.

  • Deep Learning Projects
  • Interview Questions

Related Articles

  • Machine Learning Tutorial
  • AI, ML and Data Science Tutorial