Deep Learning and Neural Networks
( 25 Modules )

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

Module #2
Mathematical Prerequisites
Linear Algebra, Calculus, and Optimization techniques for deep learning

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

Module #4
Activation Functions
Sigmoid, ReLU, Tanh, and other activation functions for neural networks

Module #5
Forward Propagation
Computing output of a neural network through forward propagation

Module #6
Backpropagation
Computing errors and updating weights through backpropagation

Module #7
Optimization Techniques
Gradient Descent, Stochastic Gradient Descent, and other optimization algorithms

Module #8
Convolutional Neural Networks (CNNs)
Introduction to CNNs, convolutional layers, and pooling layers

Module #9
CNN Architectures
AlexNet, VGGNet, GoogLeNet, and other popular CNN architectures

Module #10
Transfer Learning
Using pre-trained models for image classification and object detection

Module #11
Recurrent Neural Networks (RNNs)
Introduction to RNNs, simple RNNs, and LSTM networks

Module #12
RNN Architectures
GRU networks, Bidirectional RNNs, and Encoder-Decoder architectures

Module #13
Natural Language Processing (NLP) with RNNs
Text classification, language modeling, and sequence-to-sequence tasks

Module #14
Autoencoders and Generative Models
Introduction to autoencoders, variational autoencoders, and generative adversarial networks (GANs)

Module #15
Generative Models for Computer Vision
GANs for image generation, style transfer, and image-to-image translation

Module #16
Deep Learning for Speech Recognition
Introduction to speech recognition, acoustic models, and language models

Module #17
Deep Reinforcement Learning
Introduction to reinforcement learning, Q-networks, and policy gradients

Module #18
Deep Learning with Python and TensorFlow
Implementing deep learning models using Python and TensorFlow

Module #19
Deep Learning with Python and PyTorch
Implementing deep learning models using Python and PyTorch

Module #20
Model Evaluation and Hyperparameter Tuning
Metrics for evaluating deep learning models and hyperparameter tuning techniques

Module #21
Deep Learning for Computer Vision Applications
Object detection, segmentation, and tracking using deep learning

Module #22
Deep Learning for NLP Applications
Text classification, sentiment analysis, and question answering using deep learning

Module #23
Deep Learning for Speech and Audio Applications
Speech recognition, music classification, and audio generation using deep learning

Module #24
Advanced Deep Learning Topics
Attention mechanisms, transformers, and capsule networks

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

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Deep Learning and Neural Networks ( 25 Modules )

Deep Learning and Neural Networks
( 25 Modules )