Deep Learning
( 25 Modules )

Module #1
Introduction to Deep Learning
Overview of deep learning, history, and applications

Module #2
Mathematical Prerequisites
Review of linear algebra, calculus, and probability theory

Module #3
Neural Networks Basics
Introduction to artificial neural networks, perceptrons, and multilayer perceptrons

Module #4
Activation Functions and Backpropagation
Activation functions, backpropagation, and gradient descent

Module #5
Building and Training Neural Networks
Hands-on experience with building and training neural networks using a deep learning framework

Module #6
Convolutional Neural Networks (CNNs)
Introduction to CNNs, convolutional layers, and pooling layers

Module #7
CNN Architectures
AlexNet, VGGNet, GoogLeNet, and ResNet architectures

Module #8
Transfer Learning and Fine-tuning
Using pre-trained CNN models and fine-tuning for image classification tasks

Module #9
Recurrent Neural Networks (RNNs)
Introduction to RNNs, simple RNNs, and LSTM networks

Module #10
RNN Architectures
GRU, Bidirectional RNNs, and Encoder-Decoder models

Module #11
Sequence-to-Sequence Models
Machine translation, chatbots, and sequence-to-sequence models

Module #12
Generative Models
Introduction to generative models, GANs, and VAEs

Module #13
Autoencoders and Variational Autoencoders
Dimensionality reduction, autoencoders, and VAEs

Module #14
Generative Adversarial Networks (GANs)
GANs, DCGANs, and conditional GANs

Module #15
Deep Reinforcement Learning
Introduction to reinforcement learning, Q-learning, and policy gradients

Module #16
Deep Reinforcement Learning Algorithms
DDPG, Actor-Critic methods, and AlphaGo

Module #17
Unsupervised Learning and Clustering
K-means, hierarchical clustering, and dimensionality reduction

Module #18
Deep Learning for Natural Language Processing
Word embeddings, language models, and text classification

Module #19
Attention Mechanisms
Attention in NLP, transformers, and BERT

Module #20
Deep Learning for Computer Vision
Object detection, segmentation, and tracking

Module #21
Deep Learning Frameworks
TensorFlow, PyTorch, and Keras

Module #22
Model Evaluation and Hyperparameter Tuning
Model evaluation metrics, hyperparameter tuning, and cross-validation

Module #23
Deep Learning Deployment and Production
Model deployment, model serving, and productionization

Module #24
Ethics and Fairness in Deep Learning
Ethical considerations, bias, and fairness in deep learning models

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning career

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