NVIDIA Fundamentals of Deep Learning

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

• Understand deep learning fundamentals including neural data processing and model training
• Implement multi-class classification and CNNs for image recognition
• Apply transfer learning with pre-trained models to enhance performance

• Basic knowledge of machine learning, linear algebra, and Python
• Familiarity with TensorFlow or PyTorch is helpful but not required