Deep Learning Fundamentals
( 24 Modules )

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

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

Module #3
Introduction to Neural Networks
Basic concepts, types of neural networks, and applications

Module #4
Perceptron and Multilayer Perceptron
Single-layer and multilayer perceptrons, training algorithms

Module #5
Activation Functions
Types of activation functions, their properties, and importance

Module #6
Forward Propagation
Computing outputs, understanding the forward pass

Module #7
Backpropagation
Error computation, gradient descent, and optimization

Module #8
Optimization Techniques
Gradient descent, stochastic gradient descent, and minibatch

Module #9
Regularization Techniques
Overfitting, L1 and L2 regularization, dropout

Module #10
Convolutional Neural Networks (CNNs)
Introduction to CNNs, convolutional and pooling layers

Module #11
Transfer Learning and Pre-trained Models
Using pre-trained models, fine-tuning, and feature extraction

Module #12
Recurrent Neural Networks (RNNs)
Introduction to RNNs, simple and long short-term memory (LSTM) networks

Module #13
Long Short-Term Memory (LSTM) Networks
Understanding LSTM architecture, advantages, and applications

Module #14
Natural Language Processing (NLP) with Deep Learning
Introduction to NLP, text preprocessing, and sequence-to-sequence models

Module #15
Deep Learning for Computer Vision
Image classification, object detection, segmentation, and generation

Module #16
Autoencoders and Generative Models
Introduction to autoencoders, variational autoencoders, and generative adversarial networks (GANs)

Module #17
Deep Learning for Audio and Speech
Introduction to audio and speech processing, speech recognition, and music synthesis

Module #18
Deep Reinforcement Learning
Introduction to reinforcement learning, Q-learning, and policy gradients

Module #19
Deep Learning Frameworks
Overview of popular deep learning frameworks (TensorFlow, PyTorch, Keras)

Module #20
Deep Learning for Big Data
Distributed computing, parallel processing, and big data architectures

Module #21
Deep Learning Modeling Best Practices
Model design, hyperparameter tuning, and model evaluation

Module #22
Deep Learning Deployment and Serving
Model deployment, model serving, and model inference

Module #23
Ethics and Fairness in Deep Learning
Bias and fairness, ethical considerations, and explainable AI

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning Fundamentals career

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Deep Learning Fundamentals ( 24 Modules )

Deep Learning Fundamentals
( 24 Modules )