Deep Learning

Uncover the power of Deep Learning and neural networks.

Module 1: Introduction to Deep Learning

  • What is Deep Learning?
  • Key Concepts: Neurons, Activation Functions, Layers
  • Difference Between Machine Learning and Deep Learning
  • Applications of Deep Learning in Real World

Module 2: Mathematics for Deep Learning

  • Linear Algebra Basics: Vectors, Matrices, Tensors
  • Calculus Basics: Derivatives, Chain Rule
  • Probability and Statistics for DL
  • Gradient Descent and Optimization Algorithms

Module 3: Neural Networks Basics

  • Introduction to Artificial Neural Networks (ANN)
  • Structure of Neural Networks: Layers, Weights, Biases
  • Forward Propagation and Backpropagation
  • Types of Activation Functions: Sigmoid, ReLU, Tanh, Softmax

Module 4: Deep Learning Frameworks

  • Introduction to TensorFlow
  • Introduction to PyTorch
  • Building and Training a Neural Network in TensorFlow
  • Building and Training a Neural Network in PyTorch

Module 5: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs
  • Convolution Operations
  • Pooling Layers: Max Pooling, Average Pooling
  • Architectures of CNNs: VGG, ResNet, Inception
  • Transfer Learning with Pre-Trained Models

Module 6: Recurrent Neural Networks (RNNs)

  • Introduction to RNNs
  • Sequence Data and Time-Series Analysis
  • Variants of RNN: LSTM, GRU
  • Applications of RNNs: Text Generation, Sentiment Analysis

Module 7: Generative Models

  • Introduction to Generative Models
  • Autoencoders
  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Applications of GANs: Image Synthesis, Style Transfer

Module 8: Natural Language Processing (NLP) with Deep Learning

  • Word Embeddings: Word2Vec, GloVe
  • Text Preprocessing for Deep Learning
  • Sequence-to-Sequence Models
  • Transformers and Attention Mechanisms
  • Applications: Machine Translation, Chatbots

Module 9: Advanced Topics in Deep Learning

  • Hyperparameter Tuning and Regularization Techniques
  • Optimization Algorithms: Adam, RMSprop, SGD
  • Explainable Deep Learning: Grad-CAM, SHAP
  • Handling Imbalanced Data in Deep Learning
  • Deployment of Deep Learning Models

Module 10: Capstone Project and Revision

  • Capstone Project: End-to-End Deep Learning Application (e.g., Image Classification, Text Generation)
  • Review of Key Concepts
  • Final Assessment: Project Presentation

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