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1332 reviews

Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!

Next-Gen Computer Vision: YOLOv8, DINO-GPT4V, OpenCV4, Face Recognition, GenerativeAI, Diffusion Models & Transformers
Course details
Video 28 hours
Lectures 25
Certificate of Completion


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed
  • Description
  • Curriculum
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Welcome to Modern Computer Vision Tensorflow, Keras & PyTorch!

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

Update for 2024: 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 There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations.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:

  1. PyTorch & Keras CNN Tutorial MNIST

  2. PyTorch & Keras Misclassifications and Model Performance Analysis

  3. PyTorch & Keras Fashion-MNIST with and without Regularisation

  4. CNN Visualisation – Filter and Filter Activation Visualisation

  5. CNN Visualisation Filter and Class Maximisation

  6. CNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAM

  7. Replicating LeNet and AlexNet in Tensorflow2.0 using Keras

  8. PyTorch & Keras Pretrained Models – 1 – VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNet

  9. Rank-1 and Rank-5 Accuracy

  10. PyTorch and Keras Cats vs Dogs PyTorch – Train with your own data

  11. PyTorch Lightning Tutorial – Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and more

  12. PyTorch Lightning – Transfer Learning

  13. PyTorch and Keras Transfer Learning and Fine Tuning

  14. PyTorch & Keras Using CNN’s as a Feature Extractor

  15. PyTorch & Keras – Google Deep Dream

  16. PyTorch Keras – Neural Style Transfer + TF-HUB Models

  17. PyTorch & Keras Autoencoders using the Fashion-MNIST Dataset

  18. PyTorch & Keras – Generative Adversarial Networks – DCGAN – MNIST

  19. Keras – Super Resolution SRGAN

  20. Project – Generate_Anime_with_StyleGAN

  21. CycleGAN – Turn Horses into Zebras

  22. ArcaneGAN inference

  23. PyTorch & Keras Siamese Networks

  24. Facial Recognition with VGGFace in Keras

  25. PyTorch Facial Similarity with FaceNet

  26. DeepFace – Age, Gender, Expression, Headpose and Recognition

  27. Object Detection – Gun, Pistol Detector – Scaled-YOLOv4

  28. Object Detection – Mask Detection – TensorFlow Object Detection – MobileNetV2 SSD

  29. Object Detection  – Sign Language Detection – TFODAPI – EfficientDetD0-D7

  30. Object Detection – Pot Hole Detection with TinyYOLOv4

  31. Object Detection – Mushroom Type Object Detection – Detectron 2

  32. Object Detection – Website Screenshot Region Detection – YOLOv4-Darknet

  33. Object Detection – Drone Maritime Detector – Tensorflow Object Detection Faster R-CNN

  34. Object Detection – Chess Pieces Detection – YOLOv3 PyTorch

  35. Object Detection – Hardhat Detection for Construction sites – EfficientDet-v2

  36. Object DetectionBlood Cell Object Detection – YOLOv5

  37. Object DetectionPlant Doctor Object Detection – YOLOv5

  38. Image Segmentation – Keras, U-Net and SegNet

  39. DeepLabV3 – PyTorch_Vision_Deeplabv3

  40. Mask R-CNN Demo

  41. Detectron2 – Mask R-CNN

  42. Train a Mask R-CNN – Shapes

  43. Yolov5 DeepSort Pytorch tutorial

  44. DeepFakes – first-order-model-demo

  45. Vision Transformer Tutorial PyTorch

  46. Vision Transformer Classifier in Keras

  47. Image Classification using BigTransfer (BiT)

  48. Depth Estimation with Keras

  49. Image Similarity Search using Metric Learning with Keras

  50. Image Captioning with Keras

  51. Video Classification with a CNN-RNN Architecture with Keras

  52. Video Classification with Transformers with Keras

  53. Point Cloud Classification – PointNet

  54. Point Cloud Segmentation with PointNet

  55. 3D Image Classification CT-Scan

  56. X-ray Pneumonia Classification using TPUs

  57. Low Light Image Enhancement using MIRNet

  58. Captcha OCR Cracker

  59. Flask Rest API – Server and Flask Web App

  60. Detectron2 – BodyPose

How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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