Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2025! (Udemy.com)

Welcome to Modern Computer Vision Tensorflow, Keras & PyTorch! 2025

AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!

Update for 2025: Modern Computer Vision Course

• We're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:

• YOLOv8: Cutting-edge Object Recognition

• DINO-GPT4V: Next-Gen Vision Models

• Meta CLIP for Enhanced Image Analysis

• Detectron2 for Object Detection

• Segment Anything

• Face Recognition Technologies

• Generative AI Networks for Creative Imaging

• Transformers in Computer Vision

• Deploying & Productionizing Vision Models

• Diffusion Models for Image Processing

• Image Generation and Its Applications

• Annotation Strategy for Efficient Learning

• Retrieval Augmented Generation (RAG)

• Zero-Shot Classifiers for Versatile Applications

• Using Roboflow: Streamlining Vision Workflows

What is Computer Vision?

But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless.

Job demand for Computer Vision workers are skyrocketing and it’s common that experts in the field are making USD $200,000 and more salaries. However, getting started in this field isn’t easy. There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations. Beginners thus have no idea where to start.

This course aims to solve all of that!

• Taught using Google Colab Notebooks (no messy installs, all code works straight away)

• 27+ Hours of up-to-date and relevant Computer Vision theory with example code

• Taught using both PyTorch and Tensorflow Keras!

In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics:

Computer vision applications involving Deep Learning are booming!

Having Machines that can see will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:

• Perform surgery and accurately analyze and diagnose you from medical scans.

• Enable self-driving cars

• Radically change robots allowing us to build robots that can cook, clean, and assist us with almost any task

• Understand what's being seen in CCTV surveillance videos thus performing security, traffic management, and a host of other services

• Create Art with amazing Neural Style Transfers and other innovative types of image generation

• Simulate many tasks such as Aging faces, modifying live video feeds, and realistically replacing actors in films

Detailed OpenCV Guide covering:

• Image Operations and Manipulations

• Contours and Segmentation

• Simple Object Detection and Tracking

• Facial Landmarks, Recognition and Face Swaps

• OpenCV implementations of Neural Style Transfer, YOLOv3, SSDs and a black and white image colorizer

• Working with Video and Video Streams

Our Comprehensive Deep Learning Syllabus includes:

• Classification with CNNs

• Detailed overview of CNN Analysis, Visualizing performance, Advanced CNNs techniques

• Transfer Learning and Fine Tuning

• Generative Adversarial Networks - CycleGAN, ArcaneGAN, SuperResolution, StyleGAN

• Autoencoders

• Neural Style Transfer and Google DeepDream

• Modern CNN Architectures including Vision Transformers (ResNets, DenseNets, MobileNET, VGG19, InceptionV3, EfficientNET and ViTs)

• Siamese Networks for image similarity

• Facial Recognition (Age, Gender, Emotion, Ethnicity)

• PyTorch Lightning

• Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs,

• Deep Segmentation - MaskCNN, U-NET, SegNET, and DeepLabV3

• Tracking with DeepSORT

• Deep Fake Generation

• Video Classification

• Optical Character Recognition (OCR)

• Image Captioning

• 3D Computer Vision using Point Cloud Data

• Medical Imaging - X-Ray analysis and CT-Scans

• Depth Estimation

• Making a Computer Vision API with Flask

• And so much more

This is a comprehensive course, is broken up into two (2) main sections. This first is a detailed OpenCV (Classical Computer Vision tutorial) and the second is a detailed Deep Learning

This course is filled with fun and cool projects including these Classical Computer Vision Projects:

1. Sorting contours by size, location, using them for shape matching

2. Finding Waldo

3. Perspective Transforms (CamScanner)

4. Image Similarity

5. K-Means clustering for image colors

6. Motion tracking with MeanShift and CAMShift

7. Optical Flow

8. Facial Landmark Detection with Dlib

9. Face Swaps

10. QR Code and Barcode Reaching

11. Background removal

12. Text Detection

13. OCR with PyTesseract and EasyOCR

14. Colourize Black and White Photos

15. Computational Photography with inpainting and Noise Removal

16. Create a Sketch of yourself using Edge Detection

17. RTSP and IP Streams

18. Capturing Screenshots as video

19. Import Youtube videos directly

    
    

Deep Learning Computer Vision Projects: