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.
Overview
Syllabus
- Foundations of Deep Learning
- Welcome to Week 1 of the NVIDIA: Fundamentals of Deep Learning course. This week, we will cover the basics of Deep Learning. We will explore how data is processed in a neuron and learn about Gradient Descent. Next, we will demonstrate Training a Perceptron and dive into Forward Propagation and Backward Propagation in deep learning networks. Finally, we will look at Activation Functions with a practical demo. By the end of the week, you will have a strong understanding of these core concepts.
- Advanced Deep Learning Techniques
- Welcome to Week 2 of NVIDIA: Fundamentals of Deep Learning course. This week, we will dive into Advanced Deep Learning Techniques, where we will learn about Multi-Class Classification using the MNIST Dataset and explore how deep learning models can be applied for classification tasks. We will cover training a multiclass classifier and methods to fit and evaluate the model's performance. Next, we will gain a deep understanding of Convolutional Neural Networks (CNNs), which are essential for image recognition tasks. We will also explore Transfer Learning Techniques, which allow us to leverage pre-trained models for new tasks. By the end of the week, we will implement Transfer Learning on an Image Dataset through a practical demo, reinforcing your understanding of these advanced techniques.
Taught by
Whizlabs Instructor
