Nvidia-Certified Associate - Generative Ai Llms (Nca-Genl)

[AD-TEAM]

Nvidia-Certified Associate - Generative Ai Llms (Nca-Genl)
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.88 GB | Duration: 18h 11m

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

[b]What you'll learn[/b]

Machine Learning Fundamentals

Deep Learning Fundamentals

Generative AI and LLMs

NVIDIA GPU Acceleration

Prompt Engineering

NCA-GENL Exam Preparation

[b]Requirements[/b]

Basic programming experience (Python recommended)

Fundamental understanding of machine learning concepts

Access to a computer with internet connectivity for online learning

[b]Description[/b]

NVIDIA Generative AI LLMs (NCA-GENL) Exam Prep: Become a Certified Generative AI SpecialistPrepare to ace the NVIDIA Generative AI LLMs (NCA-GENL) Certification exam and earn your certification as a Generative AI Specialist! This comprehensive course is designed to equip you with the in-depth knowledge and practical skills needed to excel in the world of generative AI and large language models (LLMs), leveraging NVIDIA's cutting-edge technology.What You'll Learn to Master the NCA-GENL Exam:Machine Learning Fundamentals: Solidify your understanding of machine learning principles, algorithms, and techniques, crucial for grasping the inner workings of generative AI.Deep Learning Fundamentals: Delve into deep learning architectures, neural networks, and training methodologies that empower LLMs to generate text, images, and other forms of content.Generative AI and LLMs: Gain a deep understanding of generative AI concepts, model architectures (like transformers), and the unique capabilities of large language models.NVIDIA GPU Acceleration: Harness the power of NVIDIA GPUs for accelerated model training, inference, and deployment, ensuring optimal performance and efficiency in real-world applications.Prompt Engineering: Master the art of prompt engineering, crafting precise and effective prompts to guide LLMs in producing desired outputs, from creative text generation to complex code synthesis.Real-World Applications: Explore the diverse and transformative applications of generative AI across industries, including content creation, code generation, design, chatbots, and more.NCA-GENL Exam Preparation: Receive targeted guidance and practice to confidently approach and pass the NVIDIA Generative AI LLMs (NCA-GENL) certification exam.Is This Course Right for You?This course is ideal forevelopers seeking to integrate generative AI capabilities into their applications.Data Scientists interested in harnessing the power of LLMs for text analysis, natural language processing, and data-driven insights.Machine Learning Enthusiasts eager to explore the forefront of AI research, text generation, and language processing technologies.AI Professionals aiming to enhance their skill set, advance their careers, and achieve the prestigious NVIDIA Generative AI with LLM Certification.Prerequisites:Basic programming experience (Python recommended)Fundamental understanding of machine learning conceptsAccess to a computer with internet connectivity for online learningEnroll Now and Get Certified!Prepare yourself for a rewarding career in generative AI. Gain the skills and knowledge to develop and deploy innovative AI solutions with NVIDIA's powerful technology. Pass the NCA-GENL exam with confidence and become a sought-after expert in the field.

Overview

Section 1: Introduction

Lecture 1 Welcome to the Course

Lecture 2 What makes this course Unique

Section 2: Machine Learning Fundamentals

Lecture 3 Introduction to Machine Learning Fundamentals

Lecture 4 Introduction to Machine Learning

Lecture 5 Types of Machine Learning

Lecture 6 Linear Regression & Evaluation Metrics for Regression

Lecture 7 Regularization and Assumptions of Linear Regression

Lecture 8 Logistic Regression

Lecture 9 Gradient Descent

Lecture 10 Logistic Regression Implementation and EDA

Lecture 11 Evaluation Metrics for Classification

Lecture 12 Decision Tree Algorithms

Lecture 13 Loss Functions of Decision Trees

Lecture 14 Decision Tree Algorithm Implementation

Lecture 15 Overfit Vs Underfit - Kfold Cross validation

Lecture 16 Hyperparameter Optimization Techniques

Lecture 17 KNN Algorithm

Lecture 18 SVM Algorithm

Lecture 19 Ensemble Learning - Voting Classifier

Lecture 20 Ensemble Learning - Bagging Classifier & Random Forest

Lecture 21 Ensemble Learning - Boosting Adabost and Gradient Boost

Lecture 22 Emsemble Learning XGBoost

Lecture 23 Clustering - Kmeans

Lecture 24 Clustering - Hierarchial Clustering

Lecture 25 Clustering - DBScan

Lecture 26 Time Series Analysis

Lecture 27 ARIMA Hands On

Section 3: Fundamentals of Deep Learning

Lecture 28 Deep Learning Fundaments - Introduction

Lecture 29 Introduction to Deep Learning

Lecture 30 Introduction to Tensorflow & Create first Neural Network

Lecture 31 Intuition of Deep Learning Training

Lecture 32 Activation Function

Lecture 33 Architecture of Neural Networks

Lecture 34 Deep Learning Model Training. - Epochs - Batch Size

Lecture 35 Hyperparameter Tuning in Deep Learning

Lecture 36 Vanshing & Exploding Gradients - Initializations, Regularizations

Lecture 37 Introduction to Convolutional Neural Networks

Lecture 38 Implementation of CNN on CatDog Dataset

Lecture 39 Transfer Learning for Computer Vision

Lecture 40 Feed Forward Neural Network Challenges

Lecture 41 RNN & Types of Architecture

Lecture 42 LSTM Architecture

Lecture 43 Attention Mechanism

Lecture 44 Transfer Learning for Natural Language Data

Section 4: Essentials of NLP

Lecture 45 Introduction to NLP Section

Lecture 46 Introduction to NLP and NLP Tasks

Lecture 47 Understanding NLP Pipeline

Lecture 48 Text Preprocessing Techniques - Tokenization

Lecture 49 Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization

Lecture 50 Feature Extraction - NLP

Lecture 51 One Hot Encoding Technique

Lecture 52 Bag of Words & Count Vectorizer

Lecture 53 TF IDF Score

Lecture 54 Word Embeddings

Lecture 55 CBoW and Skip gram - word embeddings

Section 5: Large Language Models

Lecture 56 Introduction to Large Language Models

Lecture 57 How Large Language Models (LLMs) are trained

Lecture 58 Capabilities of LLMs

Lecture 59 Challenges of LLMs

Lecture 60 Introduction to Transformers - Attention is all you need

Lecture 61 Positional Encodings

Lecture 62 Positional Encodings - Deep Dive

Lecture 63 Self Attention & Multi Head Attention

Lecture 64 Self Attention & Multi Head Attention - Deep Dive

Lecture 65 Understanding Masked Multi Head Attention

Lecture 66 Masked Multi Head Attention - Deep Dive

Lecture 67 Encoder Decoder Architecture

Lecture 68 Customization of LLMs - Prompt Engineering

Lecture 69 Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning

Lecture 70 Difference between Prompt Tuning and p-tuning

Lecture 71 PEFT - Parameter Efficient Fine Tuning

Lecture 72 Training data for LLMs

Lecture 73 Pillars of LLM Training Data: Quality, Diversity, and Ethics

Lecture 74 Data Cleaning for LLMs

Lecture 75 Biases in Large Language Models

Lecture 76 Loss Functions for LLMs

Section 6: Prompt Engineering for the NCA-GENL Exam

Lecture 77 What is Prompt Engineering ?

Lecture 78 Advanced Prompt Engineering

Lecture 79 Techniques for Effective Prompts

Lecture 80 Ethical Considerations in Prompt Design for Large Language Models

Lecture 81 NVIDIA's Tools and Frameworks for Prompt Engineering

Lecture 82 NVIDIA Ecosystem tools for LLM Model Training

Section 7: Data Analysis and Visualization

Lecture 83 Data Visualization & Analysis of LLMs

Lecture 84 EDA for LLMs

Section 8: Experimentation

Lecture 85 Experiment Design Principles for LLMs

Lecture 86 Techniques for Large Language Models Experimentation

Lecture 87 Data Management and Version Control for LLM experimentation

Lecture 88 NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont

Section 9: LLM integration & Deployment

Lecture 89 LLM Integration and Deployment

Lecture 90 Deployment Considerations for Large Language Models

Lecture 91 Monitoring and Maintenance of Large Language Models

Lecture 92 Explainability and Interpretability of Large Language Models

Lecture 93 NVIDIA Ecosystem Tools for Deployment and Integration

Section 10: Trustworthy AI

Lecture 94 Building Trustworthy AI & NVIDIA Tools

Lecture 95 Trustworthy AI - Exam Guide

Section 11: Important - Exam Scheduling - Exam Registration Guide

Lecture 96 Exam Tips & Instructions - watch this completely

Developers seeking to integrate generative AI capabilities into their applications.,Data Scientists interested in harnessing the power of LLMs for text analysis, natural language processing, and data-driven insights.,Machine Learning Enthusiasts eager to explore the forefront of AI research, text generation, and language processing technologies.,AI Professionals aiming to enhance their skill set, advance their careers, and achieve the prestigious NVIDIA Generative AI with LLM Certification.

[To see links please register or login]

[To see links please register or login]