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Module 01: Fundamentals of Neural Networks
1.0 Introduction: Fundamentals of Neural Networks
1.1 Introduction: Introduction to Neural Networks
1.2 Basic Concepts of Neural Networks
1.3 Types of Neural Networks
1.4 Limitations & Applications of Neural Networks
1.5 Introduction: Neural Network Architecture
1.6 Architecture Components
1.7 Building Neural Networks
1.8 Common Design Patterns
1.9 Introduction: Hands-on: Implement a Basic Neural Network
1.10 Hands-on Exercises
1.11 Summary: Fundamentals of Neural Networks
Lab Practice 1.1
Lab Practice 1.2
Lab Practice 1.3
Lab Practice 1.4
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Module 1: Fundamentals of Neural Networks
Quiz
Module 02 : Neural Network Optimization
2.0 Introduction: Neural Network Optimization
2.1 Introduction:Hyperparameter Tuning
2.2 Importance of Hyperparameters
2.3 Tuning Techniques
2.4 Introduction: Optimization Algorithms
2.5 Types of Optimization Algorithms
2.6 Choosing the Right Algorithm
2.7 Introduction: Regularization Techniques
2.8 Preventing Overfitting
2.9 Other Methods for Model Robustness
2.10 Introduction: Hands-on: Hyperparameter Tuning and Optimization
2.11 Task Description
2.12 Practical Activities
2.13 Evaluation and Analysis
2.14 Summary: Neural Network Optimization
Lab Practice 2.1
Lab Practice 2.2
Lab Practice 2.3
Python File Download
Module 2: Neural Network Optimization
Quiz
Module 03 : Neural Network Architectures for NLP
3.0 Introduction: Neural Network Architectures for NLP
3.1 Introduction: Key NLP Concepts
3.2 NLP Fundamentals
3.3 Tokenization and Embedding
3.4 Introduction: NLP-Specific Architectures
3.5 RNNs and LSTMs
3.6 Transformer-Based Architectures
3.7 Introduction: Hands-on: Implementing an NLP Model
3.8 Summary: Neural Network Architectures for NLP
Lab Practice 3.1
Python File Download
Module 3: Neural Network Architectures for NLP
Quiz
Module 04: Neural Network Architectures for Computer Vision
4.0 Introduction: Neural Network Architectures for Computer Vision
4.1 Introduction: Key Computer Vision Concepts
4.2 Computer Vision Fundamentals
4.3 Convolutional Neural Networks (CNNs)
4.4 Introduction: Computer Vision-Specific Architectures
4.5 Specialized Architectures for Computer Vision
4.6 Techniques for Object Detection and Image Segmentation
4.7 Introduction: Hands-on: Building a Computer Vision Model
4.8 Developing a Simple CNN for Image Classification
4.9 Experimenting with Object Detection Techniques
4.10 Summary: Neural Network Architectures for Computer Vision
Lab Practice 4.1
Lab Practice 4.2
Python File Download
Module 4: Neural Network Architectures for Computer Vision
Quiz
Module 05: Model Evaluation and Performance Metrics
5.0 Introduction: Model Evaluation and Performance Metrics
5.1 Introduction: Model Evaluation Techniques
5.2 Evaluation Metrics for AI Models
5.3 Cross-Validation and Model Selection
5.4 Introduction: Improving Model Performance
5.5 Bias and Variance and Addressing Overfitting and Underfitting
5.6 Techniques for Performance Optimization
5.7 Introduction: Hands-on Evaluation and optimising AI Modules
5.8 Summary: Model Evaluation and Performance Metrics
Lab Practice 5.1
Lab Practice 5.2
Lab Practice 5.3
Lab Practice 5.4
Lab Practice 5.5
Lab Practice 5.6
Lab Practice 5.7
Lab Practice 5.8
Python File Download
Module 5: Model Evaluation and Performance Metrics
Quiz
Module 06: AI Infrastructure and Deployment
6.0 Introduction: AI Infrastructure and Deployment
6.1 Introduction: Infrastructure for AI Development
6.2 Hardware Requirements
6.3 Cloud-Based AI Services
6.4 Model Deployment Techniques
6.5 Monitoring and Maintenance
6.6 Introduction: Hands-on: Deploying an AI Model
6.7 Summary: AI Infrastructure and Deployment
Lab Practice 6.1
Python File Download
Module 6: AI Infrastructure and Deployment
Quiz
Module 07: AI Ethics and Responsible AI Design
7.0 Introduction: AI Ethics and Responsible AI Design
7.1 Introduction: Ethical Considerations in AI
7.2 Bias, Fairness, and Accountability
7.3 Explainability and Transparency
7.4 Ensuring Ethical AI Development
7.5 Case Studies in AI Ethics
7.6 Introduction: Hands-on: Analyzing Ethical Considerations in AI
7.7 Summary: AI Ethics and Responsible AI Design
Lab Practice 7.1
Lab Practice 7.2
Python File Download
Module 7: AI Ethics and Responsible AI Design
Quiz
Module 08: Generative AI Models
8.0 Introduction: Generative AI Models
8.1 Introduction: Overview of Generative AI Model
8.2 Generative Adversarial Networks (GANs)
8.3 Transformer-based Models
8.4 GANs for Visual and Multimedia Artifacts
8.5 Transformer-based Models for Text Generation
8.6 Building a Simple GAN
8.7 Summary: Generative AI Models
Lab Practice 8.1
Lab Practice 8.2
Lab Practice 8.3
Lab Practice 8.4
Lab Practice 8.5
Python File Download
Module 8: Generative AI Models
Quiz
Module 9: Research-Based AI Design
9.0 Introduction: Research-Based AI Design
9.1 AI Research Techniques
9.2 Research Methodologies in AI
9.3 Interpreting Research Papers
9.4 Cutting-Edge AI Design
9.5 Exploring Recent AI Research
9.6 Applying Research to AI Design
9.7 Hands-on: Analyzing AI Research Papers
9.8 Task Description
9.9 Practical Exercises
9.10 Summary:Research-Based AI Design
Lab Practice 9.1
Python File Download
Module 9: Research-Based AI Design
Module 10: Capstone Project and Course Review
10.0 Introduction: Capstone Project and Course Review
10.1 Capstone Project Presentation
10.2 Presentation of Capstone Projects
10.3 Course Review and Future Directions
10.4 Comprehensive Course Review
10.5 Exploring Future Directions in AI
10.6 Hands-on: Capstone Project Development
10.7 Task Description
10.8 Practical Exercises
10.9 Summary: Capstone Project and Course Review
Lab Practice 10.1
Lab Practice 10.2
Lab Practice 10.3
Python File Download
Module 10: Capstone Project and Course Review
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