Why this course?

Description

This course provides a comprehensive guide to AI and deep learning with a focus on NVIDIA technologies. Students will explore powerful tools like Jetson Nano, CUDA, TensorFlow, PyTorch, and various computer vision techniques to develop and train AI models. The course covers a range of topics, from deep learning fundamentals to advanced techniques like object detection and image segmentation, preparing students to work on real-world AI projects and deploy models effectively.

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Course Overview: (10 hours 12 minutes)

• Total Duration: 10 hours 12 minutes 45 seconds
• Total Sessions: 51
• Suggested Time to Complete: 18 days (approximately 30 minutes - 1 hour per day)

The course covers various essential aspects of AI, machine learning, and computer vision:

• Introduction to NVIDIA and Jetson Nano setup.
• Deep learning fundamentals, frameworks, and neural network development.
• Computer vision techniques, including image processing and lane detection.
• Advanced topics like object detection using YOLO, image segmentation, and converting models with TensorRT.

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Project Overview:

Throughout the course, you will engage in hands-on projects like:

1. Traffic Sign Classification using Deep Learning:
   • Apply CNNs using TensorFlow and PyTorch to classify traffic signs.
2. Lane Detection using Hough Lines:
   • Use computer vision techniques to detect lanes in road images.
3. Brain Tumor Classification using Pretrained Models:
   • Train and classify brain tumor images using pretrained models.
4. Object Detection with YOLO:
   • Implement YOLO for object detection and recognition.
5. Image Segmentation with U-net:
   • Apply image segmentation techniques to extract important features from images.

These projects will help solidify your understanding of the course concepts by applying them in practical scenarios.

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Tools Covered:

1. NVIDIA Jetson Nano – A powerful edge computing platform for AI development.
2. CUDA – A parallel computing platform for accelerating AI model training.
3. TensorFlow – A popular deep learning framework for developing neural networks.
4. PyTorch – An open-source deep learning library known for its flexibility and ease of use.
5. OpenCV – A computer vision library for image processing and feature extraction.
6. Pretrained Models – Use of VGG, ResNet, F-CNN, and U-Net models for transfer learning.
7. YOLO (You Only Look Once) – A fast and accurate object detection algorithm.
8. TensorRT – A platform for optimizing and deploying deep learning models.

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Skills Required:

• Basic Python Programming – Understanding of Python, as it is the primary language used in this course.
• Deep Learning Foundations – Basic knowledge of neural networks, CNNs, and backpropagation.
• Familiarity with TensorFlow and PyTorch – While not mandatory, some prior exposure to these frameworks will be helpful.
• Computer Vision Basics – Understanding of image processing techniques and familiarity with libraries like OpenCV.
• Hardware Knowledge – Basic knowledge of working with the Jetson Nano or similar edge devices (covered in the course).

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Day 1:

• AI with NVIDIA (35 min 58 sec)

Day 2:

• Jetson Nano Basic Setup Tutorial (36 min 5 sec)

Day 3:

• Introduction to CUDA, CUDA Memory Hierarchy (36 min 31 sec)

Day 4:

• Deep Learning Fundamentals, Introduction to Deep Learning (32 min 20 sec)

Day 5:

• Introduction to Deep Learning Frameworks (TensorFlow, PyTorch) with GPU Support (55 min 45 sec)

Day 6:

• Building and Training a Simple Neural Network using PyTorch and TensorFlow (50 min 20 sec)

Day 7:

• Convolutional Neural Networks (CNNs), CNN using TensorFlow and PyTorch (36 min 3 sec)

Day 8:

• Traffic Sign Classification using Deep Learning (36 min 57 sec)

Day 9:

• Introduction to Computer Vision and Basic Image Processing (33 min 48 sec)

Day 10:

• Image Smoothing, Edge Detection, and Morphology Techniques (33 min 21 sec)

Day 11:

• Advanced Image Segmentation and Thresholding Techniques (25 min 25 sec)

Day 12:

• Image Blending, Pyramids, and Feature Transform Techniques (51 min 52 sec)

Day 13:

• Lane Detection, Hough Lines (29 min 6 sec)

Day 14:

• Pretrained Models Overview - VGG, ResNet, F-CNN, U-Net (31 min 19 sec)

Day 15:

• Brain Tumor Classification using Pretrained Models (28 min 33 sec)

Day 16:

• YOLO for Object Detection (30 min 58 sec)

Day 17:

• Image Segmentation with U-Net (28 min 17 sec)

Day 18:

• PyTorch Model to TensorRT Conversion (Sessions: 2)