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Nvidia-Certified Associate - Generative Ai Llms (Nca-Genl) - AD-TEAM - 09-09-2024 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. |