NVIDIA-Certified Associate – Generative AI LLMs (NCA-GENL)

سرفصل های دوره

Become an NVIDIA Certified Generative AI Specialist (NCA-GENL Exam Prep)

1. Introduction

1. Welcome to the Course

2. What makes this course Unique

2. Machine Learning Fundamentals

1. Introduction to Machine Learning Fundamentals

2. Introduction to Machine Learning

3. Types of Machine Learning

4. Linear Regression & Evaluation Metrics for Regression

5. Regularization and Assumptions of Linear Regression

6. Logistic Regression

7. Gradient Descent

8. Logistic Regression Implementation and EDA

9. Evaluation Metrics for Classification

10. Decision Tree Algorithms

11. Loss Functions of Decision Trees

12. Decision Tree Algorithm Implementation

13. Overfit Vs Underfit - Kfold Cross validation

14. Hyperparameter Optimization Techniques

15. KNN Algorithm

16. SVM Algorithm

17. Ensemble Learning - Voting Classifier

18. Ensemble Learning - Bagging Classifier & Random Forest

19. Ensemble Learning - Boosting Adabost and Gradient Boost

20. Emsemble Learning XGBoost

21. Clustering - Kmeans

22. Clustering - Hierarchial Clustering

23. Clustering - DBScan

24. Time Series Analysis

25. ARIMA Hands On

3. Fundamentals of Deep Learning

1. Deep Learning Fundaments - Introduction

2. Introduction to Deep Learning

3. Introduction to Tensorflow & Create first Neural Network

4. Intuition of Deep Learning Training

5. Activation Function

6. Architecture of Neural Networks

7. Deep Learning Model Training. - Epochs - Batch Size

8. Hyperparameter Tuning in Deep Learning

9. Vanshing & Exploding Gradients - Initializations, Regularizations

10. Introduction to Convolutional Neural Networks

11. Implementation of CNN on CatDog Dataset

12. Transfer Learning for Computer Vision

13. Feed Forward Neural Network Challenges

14. RNN & Types of Architecture

15. LSTM Architecture

16. Attention Mechanism

17. Transfer Learning for Natural Language Data

4. Essentials of NLP

1. Introduction to NLP Section

2. Introduction to NLP and NLP Tasks

3. Understanding NLP Pipeline

4. Text Preprocessing Techniques - Tokenization

5. Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization

6. Feature Extraction - NLP

7. One Hot Encoding Technique

8. Bag of Words & Count Vectorizer

9. TF IDF Score

10. Word Embeddings

11. CBoW and Skip gram - word embeddings

5. Large Language Models

1. Introduction to Large Language Models

2. How Large Language Models (LLMs) are trained

3. Capabilities of LLMs

4. Challenges of LLMs

5. Introduction to Transformers - Attention is all you need

6. Positional Encodings

7. Positional Encodings - Deep Dive

8. Self Attention & Multi Head Attention

9. Self Attention & Multi Head Attention - Deep Dive

10. Understanding Masked Multi Head Attention

11. Masked Multi Head Attention - Deep Dive

12. Encoder Decoder Architecture

13. Customization of LLMs - Prompt Engineering

14. Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning

15. Difference between Prompt Tuning and p-tuning

16. PEFT - Parameter Efficient Fine Tuning

17. Training data for LLMs

18. Pillars of LLM Training Data Quality, Diversity, and Ethics

19. Data Cleaning for LLMs

20. Biases in Large Language Models

21. Loss Functions for LLMs

6. Prompt Engineering for the NCA-GENL Exam

1. What is Prompt Engineering

2. Advanced Prompt Engineering

3. Techniques for Effective Prompts

4. Ethical Considerations in Prompt Design for Large Language Models

5. NVIDIAs Tools and Frameworks for Prompt Engineering

6. NVIDIA Ecosystem tools for LLM Model Training

7. Data Analysis and Visualization

1. Data Visualization & Analysis of LLMs

2. EDA for LLMs

8. Experimentation

1. Experiment Design Principles for LLMs

2. Techniques for Large Language Models Experimentation

3. Data Management and Version Control for LLM experimentation

4. NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont

9. LLM integration & Deployment

1. LLM Integration and Deployment

2. Deployment Considerations for Large Language Models

3. Monitoring and Maintenance of Large Language Models

4. Explainability and Interpretability of Large Language Models

5. NVIDIA Ecosystem Tools for Deployment and Integration

10. Trustworthy AI

1. Building Trustworthy AI & NVIDIA Tools

2. Trustworthy AI - Exam Guide

11. Important - Exam Scheduling - Exam Registration Guide

1. Exam Tips & Instructions - watch this completely

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