Deep Learning Algorithms and Models
( 30 Modules )

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

Module #2
Mathematical Foundations of Deep Learning
Review of linear algebra, calculus, and probability theory

Module #3
Building Blocks of Deep Learning:Perceptrons and Multilayer Perceptrons
Introduction to artificial neural networks and perceptrons

Module #4
Activation Functions and Backpropagation
Understanding activation functions and backpropagation algorithm

Module #5
Convolutional Neural Networks (CNNs) Basics
Introduction to convolutional neural networks and their applications

Module #6
CNNs for Image Classification
Building CNNs for image classification tasks

Module #7
Transfer Learning and Fine-tuning
Using pre-trained models and fine-tuning for specific tasks

Module #8
Recurrent Neural Networks (RNNs) Basics
Introduction to recurrent neural networks and their applications

Module #9
RNNs for Natural Language Processing (NLP)
Building RNNs for NLP tasks such as language modeling and text classification

Module #10
Long Short-Term Memory (LSTM) Networks
Understanding LSTM networks and their applications

Module #11
Generative Adversarial Networks (GANs) Basics
Introduction to generative adversarial networks and their applications

Module #12
GANs for Image Generation and Data Imputation
Building GANs for image generation and data imputation tasks

Module #13
Deep Learning for NLP:Word Embeddings and Language Models
Understanding word embeddings and language models

Module #14
Deep Learning for Computer Vision:Object Detection and Segmentation
Building models for object detection and segmentation tasks

Module #15
Autoencoders and Variational Autoencoders
Understanding autoencoders and variational autoencoders

Module #16
Deep Reinforcement Learning
Introduction to deep reinforcement learning and its applications

Module #17
Deep Learning for Time Series Analysis
Building models for time series analysis tasks

Module #18
Deep Learning for Recommendation Systems
Understanding deep learning models for recommendation systems

Module #19
Deep Learning for Healthcare and Bioinformatics
Applications of deep learning in healthcare and bioinformatics

Module #20
Deep Learning for Robotics and Autonomous Vehicles
Applications of deep learning in robotics and autonomous vehicles

Module #21
Advanced Topics in Deep Learning:Attention Mechanisms
Understanding attention mechanisms in deep learning

Module #22
Advanced Topics in Deep Learning:Transformers
Understanding transformers in deep learning

Module #23
Deep Learning Model Evaluation and Hyperparameter Tuning
Evaluating and tuning deep learning models

Module #24
Deep Learning Computing and Hardware
Overview of deep learning computing and hardware

Module #25
Deep Learning ethics and Fairness
Understanding ethics and fairness in deep learning

Module #26
Deep Learning Project Development and Deployment
Building and deploying deep learning projects

Module #27
Deep Learning Case Studies and Applications
Real-world case studies and applications of deep learning

Module #28
Deep Learning Research and Future Directions
Research and future directions in deep learning

Module #29
Deep Learning Toolkits and Frameworks
Overview of popular deep learning toolkits and frameworks

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning Algorithms and Models career

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Deep Learning Algorithms and Models ( 30 Modules )

Deep Learning Algorithms and Models
( 30 Modules )