Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs

The NVIDIA: Fundamentals of Deep Learning Course is the second course in the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs Associate specialization. It introduces learners to core deep learning concepts and techniques, building on foundational machine learning principles. The course covers neuron data processing, gradient descent, Perceptron training, forward and backward propagation, activation functions, and advanced techniques like multi-class classification and Convolutional Neural Networks (CNNs). Learners will also explore transfer learning through a hands-on demo. This course is structured into two modules, with each module containing Lessons and Video Lectures. Learners will engage with approximately 3:30-4:00 hours of video content, covering both theoretical concepts and hands-on practice. Each module includes quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Foundations of Deep Learning Module 2: Advanced Deep Learning Techniques By the end of this course, a learner will be able to: - Understand deep learning fundamentals, including neuron data processing and model training. - Implement multi-class classification and CNNs for image recognition tasks. - Apply transfer learning with pre-trained models to improve deep learning performance. This course is designed for individuals looking to enhance their skills in deep learning, particularly those aiming to work with generative AI models and LLMs. It is ideal for AI practitioners, data scientists, and machine learning engineers seeking a structured approach to mastering deep learning concepts.

NVIDIA: Fundamentals of Machine Learning Course is a foundational course designed to introduce learners to key machine learning concepts and techniques. This course is the first part of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs Associate specialization. The course covers fundamental machine learning principles, including supervised and unsupervised learning, model training, evaluation metrics, and optimization techniques. It also provides insights into data preprocessing, feature engineering, and common machine learning algorithms. This course is structured into three modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: ML Basics and Data Preprocessing Module 2: Supervised Learning & Model Evaluation Module 3: Unsupervised Learning, Advanced Techniques & GPU Acceleration By the end of this course, a learner will be able to: - Understand the fundamentals of AI, ML, and Deep Learning, and their key differences. - Implement supervised learning techniques like classification and regression. - Apply clustering methods and time series analysis using ARIMA. - Leverage NVIDIA RAPIDS for GPU-accelerated ML workflows. This course is intended for individuals looking to enhance their machine-learning skills, particularly those interested in GPU-accelerated AI workflows and NVIDIA technologies.

NVIDIA: Fundamentals of NLP and Transformers Course is the third course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course provides learners with foundational knowledge of Natural Language Processing (NLP) and practical skills for working with NLP pipelines and transformer models. It combines theoretical concepts with hands-on exercises to prepare learners for real-world NLP applications. This course covers key NLP topics, including tokenization, text preprocessing techniques, and word embeddings, along with the challenges of handling textual data. Learners will also explore sequence models (RNN, LSTM, GRU) and transformer architectures, gaining practical insights into self-attention mechanisms and encoder-decoder models. The course is structured into two modules, each comprising Lessons and Video Lectures. Learners will engage with approximately 3:00-3:30 hours of video content, covering both theoretical foundations and hands-on practice. Each module includes quizzes to reinforce learning and assess understanding. Course Modules: Module 1: Introduction to NLP: Concepts, Techniques, and Applications Module 2: Sequence Models and Transformers By the end of this course, a learner will be able to: - Understand NLP fundamentals, key tasks, and real-world applications. - Implement NLP techniques, including tokenization, word embeddings, and sequence models. - Explore transformer architecture, self-attention mechanisms, and encoder-decoder models. This course is intended for individuals interested in developing NLP expertise and working with transformer-based models. It is ideal for data scientists, machine learning engineers, and AI specialists seeking hands-on experience in modern NLP techniques.

NVIDIA: Advanced LLM Experimentation, Deployment, and Ethical AI is the sixth course in the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course equips learners with advanced knowledge on experimenting with Large Language Models (LLMs), optimizing them for deployment, and understanding the ethical considerations in AI systems. The course covers key topics such as hyperparameter tuning, A/B testing, version control, and NVIDIA tools like BioNeMo, Triton, and TensorRT. Learners will also gain insights into optimizing AI workflows using cuOpt, NGC, and Merlin. Ethical AI principles, data privacy, and minimizing bias are emphasized to ensure trustworthiness in AI systems. Course Structure: The course is divided into three modules, each containing lessons and video lectures. Learners will engage with approximately 4:30-5:00 hours of video content, combining both theory and hands-on practice. Each module is complemented with quizzes to assess comprehension and reinforce learning. Module 1: Experimentation and Hyperparameter Tuning Module 2: NVIDIA AI Services and Optimization Module 3: Ethical AI and Trustworthiness By the end of this course, learners will be able to: - Experiment with LLMs using hyperparameter tuning and A/B testing. - Apply version control and optimize AI workflows with NVIDIA tools like BioNeMo, Triton, and TensorRT. - Understand ethical AI principles, data privacy, and methods to minimize bias and enhance AI trustworthiness. This course is ideal for AI researchers, developers, and practitioners looking to enhance their skills in LLM experimentation, optimization, and ethical AI.

NVIDIA: Large Language Models and Generative AI Deployment is the fourth course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course offers a comprehensive understanding of Large Language Models (LLMs) and Generative AI deployment, combining theoretical insights with practical skills. Learners will explore key components of Generative AI, data requirements, and cleaning techniques for LLMs. The course covers model training, optimization, and evaluation methods, including Few-shot, Zero-shot, and Instruction Tuning. Additionally, the course dives into loss functions, alignment techniques, and evaluation metrics such as Perplexity. It also emphasizes the use of GPUs for training, fine-tuning methods like prompt tuning, and Parameter Efficient Fine Tuning (PEFT). Learners will gain expertise in LLM deployment strategies and monitoring with ONNX. This course is divided into three modules, each containing lessons and video lectures. Learners will engage with 4:30-5:00 hours of video content, covering both theoretical concepts and hands-on practices. Each module is equipped with quizzes to reinforce learning and assess understanding. Module 1: Fundamentals of Large Language Models Module 2: Training, Optimization, and Evaluation of LLMs Module 3: LLM Deployment Strategies and Monitoring By the end of this course, a learner will be able to: - Understand the foundational concepts of LLMs, including NLP and training data. - Explore model optimization techniques like loss functions, alignment, and PEFT. - Implement deployment strategies for LLMs and monitor performance using ONNX. This course is intended for professionals looking to deepen their expertise in deploying and optimizing LLMs for Generative AI applications.