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

Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!, available at $94.99, has an average rating of 4.34, with 255 lectures, based on 1505 reviews, and has 12703 subscribers.

You will learn about All major Computer Vision theory and concepts (updated in late 2023!) Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks YOLOv8: Cutting-edge Object Recognition DINO-GPT4V: Next-Gen Vision Models Learn all major Object Detection Frameworks from YOLOv8, R-CNNs, Detectron2, SSDs, EfficientDetect and more! Deep Segmentation with Segment Anything, U-Net, SegNet and DeepLabV3 Understand what CNNs see by Visualizing Different Activations and applying GradCAM Generative Adverserial Networks (GANs) & Autoencoders – Generate Digits, Anime Characters, Transform Styles and implement Super Resolution Training, fine tuning and analyzing your very own Classifiers Facial Recognition along with Gender, Age, Emotion and Ethnicity Detection Neural Style Transfer and Google Deep Dream Transfer Learning, Fine Tuning and Advanced CNN Techniques Important Modern CNNs designs like ResNets, InceptionV3, DenseNet, MobileNet, EffiicentNet and much more! Tracking with DeepSORT Siamese Networks, Facial Recognition and Analysis (Age, Gender, Emotion and Ethnicity) Image Captioning, Depth Estimination and Vision Transformers Point Cloud (3D data) Classification and Segmentation Making a Computer Vision API and Web App using Flask OpenCV4 in detail, covering all major concepts with lots of example code All Course Code works in accompanying Google Colab Python Notebooks Meta CLIP for Enhanced Image Analysis This course is ideal for individuals who are College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects) or Software Developers and Engineers looking to transition into Computer Vision or Start up founders lookng to learn how to implement thier big idea or Hobbyist and even high schoolers looking to get started in Computer Vision It is particularly useful for College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects) or Software Developers and Engineers looking to transition into Computer Vision or Start up founders lookng to learn how to implement thier big idea or Hobbyist and even high schoolers looking to get started in Computer Vision.

Enroll now: Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!

Summary

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

Price: $94.99

Average Rating: 4.34

Number of Lectures: 255

Number of Published Lectures: 254

Number of Curriculum Items: 255

Number of Published Curriculum Objects: 254

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

All major Computer Vision theory and concepts (updated in late 2023!)

Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks

YOLOv8: Cutting-edge Object Recognition

DINO-GPT4V: Next-Gen Vision Models

Learn all major Object Detection Frameworks from YOLOv8, R-CNNs, Detectron2, SSDs, EfficientDetect and more!

Deep Segmentation with Segment Anything, U-Net, SegNet and DeepLabV3

Understand what CNNs see by Visualizing Different Activations and applying GradCAM

Generative Adverserial Networks (GANs) & Autoencoders – Generate Digits, Anime Characters, Transform Styles and implement Super Resolution

Training, fine tuning and analyzing your very own Classifiers

Facial Recognition along with Gender, Age, Emotion and Ethnicity Detection

Neural Style Transfer and Google Deep Dream

Transfer Learning, Fine Tuning and Advanced CNN Techniques

Important Modern CNNs designs like ResNets, InceptionV3, DenseNet, MobileNet, EffiicentNet and much more!

Tracking with DeepSORT

Siamese Networks, Facial Recognition and Analysis (Age, Gender, Emotion and Ethnicity)

Image Captioning, Depth Estimination and Vision Transformers

Point Cloud (3D data) Classification and Segmentation

Making a Computer Vision API and Web App using Flask

OpenCV4 in detail, covering all major concepts with lots of example code

All Course Code works in accompanying Google Colab Python Notebooks

Meta CLIP for Enhanced Image Analysis

Who Should Attend

College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects)

Software Developers and Engineers looking to transition into Computer Vision

Start up founders lookng to learn how to implement thier big idea

Hobbyist and even high schoolers looking to get started in Computer Vision

Target Audiences

College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects)

Software Developers and Engineers looking to transition into Computer Vision

Start up founders lookng to learn how to implement thier big idea

Hobbyist and even high schoolers looking to get started in Computer Vision

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

Detectron2for Object Detection

Segment Anything

Face Recognition Technologies

Generative AI Networks for Creative Imaging

Transformersin Computer Vision

Deploying & Productionizing Vision Models

Diffusion Models for Image Processing

Image Generationand 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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Course Overview

Lecture 3: What Makes Computer Vision Hard

Lecture 4: What are Images?

Chapter 2: Download Code and Setup Colab

Lecture 1: Download Course Resources

Lecture 2: Setup – Download Code and Configure Colab

Chapter 3: OpenCV – Image Operations

Lecture 1: Getting Started with OpenCV4

Lecture 2: Grayscaling Images

Lecture 3: Colour Spaces – RGB and HSV

Lecture 4: Drawing on Images

Lecture 5: Transformations – Translations and Rotations

Lecture 6: Scaling, Re-sizing, Interpolations and Cropping

Lecture 7: Arithmetic and Bitwise Operations

Lecture 8: Convolutions, Blurring and Sharpening Images

Lecture 9: Thresholding, Binarization & Adaptive Thresholding

Lecture 10: Dilation, Erosion and Edge Detection

Chapter 4: OpenCV – Image Segmentation

Lecture 1: Contours – Drawing, Hierarchy and Modes

Lecture 2: Moments, Sorting, Approximating and Matching Contours

Lecture 3: Line, Circle and Blob Detection

Lecture 4: Counting Circles, Ellipses and Finding Waldo with Template Matching

Lecture 5: Finding Corners

Chapter 5: OpenCV – Haar Cascade Classifiers

Lecture 1: Face and Eye Detection with Haar Cascade Classifiers

Lecture 2: Vehicle and Pedestrian Detection

Chapter 6: OpenCV – Image Analysis and Transformation

Lecture 1: Perspective Transforms

Lecture 2: Histograms and K-Means Clustering for Dominant Colors

Lecture 3: Comparing Images MSE and Structual Similarity

Lecture 4: Filtering on Colour

Lecture 5: Watershed Algorithm Marker-Dased Image Segmentation

Lecture 6: Background and Foreground Subtraction

Chapter 7: OpenCV – Motion and Object Tracking

Lecture 1: Motion Tracking with Mean Shift and CAMSHIFT

Lecture 2: Object Tracking with Optical Flow

Lecture 3: Simple Object Tracking by Color

Chapter 8: OpenCV – Facial Landmark Detection & Face Swaps

Lecture 1: Facial Landmark Detection with Dlib

Lecture 2: Face Swapping with Dlib

Chapter 9: OpenCV Projects

Lecture 1: Tilt Shift Effects

Lecture 2: GrabCut Algorithm for Background Removal

Lecture 3: OCR with PyTesseract and EasyOCR (Text Detection)

Lecture 4: Barcode, QR Generation and Reading

Lecture 5: YOLOv3 in OpenCV

Lecture 6: Neural Style Transfer with OpenCV

Lecture 7: SSDs in OpenCV

Lecture 8: Colorize Black and White Photos using a Caffe Model in OpenCV

Lecture 9: Inpainting to Restore Damaged Photos

Lecture 10: Add and Remove Noise and Fix Contrast with Histogram Equalization

Lecture 11: Detect Blur in Images

Lecture 12: Facial Recognition

Chapter 10: OpenCV – Working With Video

Lecture 1: Using Your Webcam and Creating a Live Sketch of Yourself

Lecture 2: Opening Video Files in OpenCV

Lecture 3: Saving or Recording Videos in OpenCV

Lecture 4: Video Streams and CCTV – RTSP and IP

Lecture 5: Auto Reconnect to Video Streams

Lecture 6: Capturing Video using Screenshots

Lecture 7: Importing YouTube Videos into OpenCV

Chapter 11: ChatGPT4s Computer Vision Revolution and Transformers

Lecture 1: Introduction to ChatGPT

Lecture 2: Why Transformers Changed Everything!

Lecture 3: ChatGPT4 for Computer Vision Applications

Lecture 4: Understanding Embeddings and RAG

Lecture 5: Future of Generative AI

Chapter 12: GPT4V – DINO-GPT4V: Next-Gen Vision Models (2023 Update)

Lecture 1: Introduction to DINO-GPT4V

Lecture 2: Use DINO-GPT4V on Hugging Face

Lecture 3: DINO-GPT4-V: Use GPT-4V in a Two-Stage Detection Model

Chapter 13: MetaCLIP – Comparing Images

Lecture 1: How to use MetaCLIP

Lecture 2: Meta Clip Paper Explaiend – Demystifying CLIP Data

Chapter 14: Deep Learning in Computer Vision Introduction

Lecture 1: Introduction to Convolution Neural Networks

Lecture 2: Convolutions

Lecture 3: Feature Detectors

Lecture 4: 3D Convolutions and Color Images

Lecture 5: Kernel Size and Depth

Lecture 6: Padding

Lecture 7: Stride

Lecture 8: Activation Functions

Lecture 9: Pooling

Lecture 10: Fully Connected Layers

Lecture 11: Softmax

Lecture 12: Putting Together Your Convolutional Neural Network

Lecture 13: Parameter Counts in CNNs

Lecture 14: Why CNNs Work So Well On Images

Lecture 15: Training a CNN

Lecture 16: Loss Functions

Lecture 17: Backpropagation

Lecture 18: Gradient Descent

Lecture 19: Optimisers and Learning Rate Schedules

Lecture 20: Deep Learning CNN Recap

Lecture 21: Deep Learning History

Lecture 22: Deep Learning Libraries Overview

Chapter 15: Building CNNs in PyTorch

Instructors

Rajeev D. Ratan
Data Scientist, Computer Vision Expert & Electrical Engineer

Rating Distribution

1 stars: 42 votes

2 stars: 38 votes

3 stars: 125 votes

4 stars: 439 votes

5 stars: 861 votes

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