Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!
- Offered byUDEMY
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024! at UDEMY Overview
Duration | 28 hours |
Total fee | ₹399 |
Mode of learning | Online |
Credential | Certificate |
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024! at UDEMY Highlights
- 30-Day Money-Back Guarantee
- Certificate of completion
- Full lifetime access
- Learn from 3 downloadable resources and 25 articles
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024! at UDEMY Course details
- 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
- 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 CourseWe'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 RecognitionDINO-GPT4V: Next-Gen Vision ModelsMeta CLIP for Enhanced Image AnalysisDetectron2 for Object DetectionSegment AnythingFace Recognition TechnologiesGenerative AI Networks for Creative ImagingTransformers in Computer VisionDeploying & Productionizing Vision ModelsDiffusion Models for Image ProcessingImage Generation and Its ApplicationsAnnotation Strategy for Efficient LearningRetrieval Augmented Generation (RAG)Zero-Shot Classifiers for Versatile ApplicationsUsing Roboflow: Streamlining Vision WorkflowsWhat is Computer Vision?But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they are 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.
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024! at UDEMY Curriculum
Introduction
Course Introduction
Course Overview
What Makes Computer Vision Hard
What are Images?
Download Code and Setup Colab
Download Course Resources
Setup - Download Code and Configure Colab
OpenCV - Image Operations
Getting Started with OpenCV4
Grayscaling Images
Colour Spaces - RGB and HSV
Drawing on Images
Transformations - Translations and Rotations
Scaling, Re-sizing, Interpolations and Cropping
Arithmetic and Bitwise Operations
Convolutions, Blurring and Sharpening Images
Thresholding, Binarization & Adaptive Thresholding
Dilation, Erosion and Edge Detection
OpenCV - Image Segmentation
Contours - Drawing, Hierarchy and Modes
Moments, Sorting, Approximating and Matching Contours
Line, Circle and Blob Detection
Counting Circles, Ellipses and Finding Waldo with Template Matching
Finding Corners
OpenCV - Haar Cascade Classifiers
Face and Eye Detection with Haar Cascade Classifiers
Vehicle and Pedestrian Detection
OpenCV - Image Analysis and Transformation
Perspective Transforms
Histograms and K-Means Clustering for Dominant Colors
Comparing Images MSE and Structual Similarity
Filtering on Colour
Watershed Algorithm Marker-Dased Image Segmentation
Background and Foreground Subtraction
OpenCV - Motion and Object Tracking
Motion Tracking with Mean Shift and CAMSHIFT
Object Tracking with Optical Flow
Simple Object Tracking by Color
OpenCV - Facial Landmark Detection & Face Swaps
Facial Landmark Detection with Dlib
Face Swapping with Dlib
OpenCV Projects
Tilt Shift Effects
GrabCut Algorithm for Background Removal
OCR with PyTesseract and EasyOCR (Text Detection)
Barcode, QR Generation and Reading
YOLOv3 in OpenCV
Neural Style Transfer with OpenCV
SSDs in OpenCV
Colorize Black and White Photos using a Caffe Model in OpenCV
Inpainting to Restore Damaged Photos
Add and Remove Noise and Fix Contrast with Histogram Equalization
Detect Blur in Images
Facial Recognition
OpenCV - Working With Video
Using Your Webcam and Creating a Live Sketch of Yourself
Opening Video Files in OpenCV
Saving or Recording Videos in OpenCV
Video Streams and CCTV - RTSP and IP
Auto Reconnect to Video Streams
Capturing Video using Screenshots
Importing YouTube Videos into OpenCV
ChatGPT4's Computer Vision Revolution and Transformers
Introduction to ChatGPT
Why Transformers Changed Everything!
ChatGPT4 for Computer Vision Applications
Understanding Embeddings and RAG
Future of Generative AI
GPT4V - DINO-GPT4V: Next-Gen Vision Models (2023 Update)
Introduction to DINO-GPT4V
Use DINO-GPT4V on Hugging Face
DINO-GPT4-V: Use GPT-4V in a Two-Stage Detection Model
MetaCLIP - Comparing Images
How to use MetaCLIP
Meta Clip Paper Explaiend - Demystifying CLIP Data
Deep Learning in Computer Vision Introduction
Introduction to Convolution Neural Networks
Convolutions
Feature Detectors
3D Convolutions and Color Images
Kernel Size and Depth
Padding
Stride
Activation Functions
Pooling
Fully Connected Layers
Softmax
Putting Together Your Convolutional Neural Network
Parameter Counts in CNNs
Why CNNs Work So Well On Images
Training a CNN
Loss Functions
Backpropagation
Gradient Descent
Optimisers and Learning Rate Schedules
Deep Learning CNN Recap
Deep Learning History
Deep Learning Libraries Overview
Building CNNs in PyTorch
Importing Required Libraries
Transformation Pipeline
Inspect and Visualise Data
Data Loaders
Building our Model
Optimisers and Loss Function
Training Your Model
Saving Model and Displaying Results
Plot and Visualize Your Results
Building CNNs in TensorFlow with Keras
Loading Data
View and Inspect Data
Preprocessing Our Data
Constructing the CNN
Training the Model
Plotting the Training Results
Saving and Loading and Visualising Results
Assessing Model Performance
Deep Learning Libraries PyTorch vs Keras Review
Assessing Model Performance
Confusion Matrix and Classification Report
Keras Viewing Misclassifications
Keras - Confusion Matrix and Classification Report
PyTorch Viewing Misclassifications
PyTorch - Confusion Matrix and Misclassifications
Improving Models and Advanced CNN Design
What is Overfitting and Generalisation?
Introduction to Regularization
Drop Out
L1 and L2 Regularization
Data Augmentation
Early Stopping
Batch Normalization
When Do We Use Regularization
Training a Fashion Classifider (FNIST) with no Regularization using Keras
Training a Fashion Classifider (FNIST) with Regularization using Keras
Training a Fashion Classifider (FNIST) with no Regularization using PyTorch
Training a Fashion Classifider (FNIST) with Regularization using PyTorch
Visualizing What CNN's Learn
Visualizing CNN Filters or Feature Maps
Visualising Filter Activations
Keras Filter Visualization and Activations
Maximizing Filters
Class Maximization
Filter and Class Maximization
Grad-CAM Visualize What Influences Your Model
Grad-CAM Plus
Advamced Convolutional Neural Networks
History and Evolution of Convolutional Neural Networks
LeNet
AlexNet
VGG16 and VGG19
ResNets
Why ResNets Work So Well
MobileNetV1 and V2
InceptionV3
SqueezeNet
EfficientNet
DenseNet
The ImageNet Dataset
Building and Loading Advanced CNN Archiectures and Rank-N Accuracy
Implementing LeNet and AlexNet in Keras
Loading Pre-trained Networks in PyTorch (ResNets, DenseNets, MobileNET, VGG19)
Loading Pre-trained Networks in Keras (ResNets, DenseNets, MobileNET, VGG19)
The Top-N or Rank-N Accuracy Metric
Getting the Rank-N Accuracy in PyTorch
Getting the Rank-N Accuracy in Keras
Using Callbacks in Keras and PyTorch
What are Callbacks?
Cats vs Dogs Classifier using Callbacks in PyTorch
Cats vs Dogs Classifier using Callbacks in Keras
PyTorch Lightning
Introduction to PyTorch Lightning
Lightning Setup and Class
Auto Batch and Learning Rate Selection plus Tensorboards
PyTorch Lightning Calls, Saving, Inference
Training on Multiple GPU, Profiling and TPUs
Transfer Learning and Fine Tuning
Transfer Learning Introduction
Transfer Learning in PyTorch Lightning
Transfer Learning and Fine Tuning with Keras
Keras Feature Extraction
PyTorch Fine Tuning
PyTorch Transfer Learning and Freezing Network Layers
PyTorch Feature Extraction
Google DeepStream and Neural Style Transfer
Introduction to Google DeepDream Visualizations
Google DeepDream in Keras
Google DeepDream in PyTorch
Introduction to Neural Style Transfer
Neural Style Transfer in Keras
Neural Style Transfer in PyTorch
Autoencoders
Introduction to Autoencoders
Autoencoders in Keras
Autoencoders in PyTorch
Generative Adversarial Networks (GANs)
Introduction to GANs
How Do GANs Work?
Training GANs
Use Cases for GANs
Keras DCGAN with MNIST
PyTorch GANs
Super Resolution GAN
AnimeGAN
ArcaneGAN
Difusion Models (2023)
Siamese Network
Introduction to Siamese Networks
Training Siamese Networks
Siamese Networks in Keras
Siamese Networks in PyTorch
Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning
Face Recognition Overview
Facial Similarity Keras VGGFace
Face Recognition Keras One Shot Learning and Friends
Face Recognition PyTorch FaceNet
DeepFace - Age, Gender, Emotion, Ethnicity and Face Recognition
Object Detection
Object Detection
History of Object Detectors
Intersection Over Union
Mean Average Precision
Non Maximum Suppression
R-CNNs, Fast R-CNNs and Faster R-CNNs
Single Shot Detectors (SSDs)
Modern Object Detectors - YOLOv8, EfficientDet, Detectron2
Introduction to YOLO
YOLOv8
How YOLO Works
Training YOLO
YOLO Evolution
EfficientDet
Detectron2
Gun Detector - Scaled-YoloV4
Gun Detector - Scaled-YoloV4
Mask Detector TFODAPI MobileNetV2_SSD
Mask Detector TFODAPI MobileNetV2_SSD
Sign Language Detector TFODAPI EfficentDet
Sign Language Detector TFODAPI EfficentDet
Pothole Detector - TinyYOLOv4
Pothole Detector - TinyYOLOv4
Mushroom Detector Detectron2
Mushroom Detector Detectron2
Website Region Detector YOLOv4 Darknet
Website Region Detector YOLOv4 Darknet
Drone Maritime Detector R-CNN
Drone Maritime Detector R-CNN
Chess Piece YOLOv3
Chess Piece YOLOv3
Bloodcell Detector YOLOv5
Bloodcell Detector YOLOv5
Hard Hat Detector EfficentDet
Hard Hat Detector EfficentDet
Bloodcell Detector YOLOv5
Bloodcell Detector YOLOv5
Plant Doctor Detector YOLOv5
Plant Doctor Detector YOLOv5
Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN
Introduction to Deep Segmentation
Image Segmentation Keras UNET SegNet
PyTorch DeepLabV3
Mask-RCNN Tensorflow Matterport
Detectron2 Mask R-CNN
Train Mask R-CNN Shapes Dataset
Segment Anything Model (SAM)
Introduction to SAMs
Intoduction
Body Pose Estimation
Body Pose Estimation
Tracking with DeepSORT
DeepSORT Introduction
DeepSORT with YOLOv5
Deep Fakes
Creating a Deep Fake
Vision Transformers - ViTs
Introduction to Vision Transformers
Vision Transformer in Detail with PyTorch
Vision Transformers in Keras
BiT BigTransfer Classifier Keras
BiT BigTransfer Classifier Keras
Depth Estimation
Depth Estimation Project
Image Similarity using Metric Learning
Image Similarity using Metric Learning
Image Captioning with Keras
Image Captioning with Keras
Video Classification usign CNN+RNN
Video Classification usign CNN+RNN
Video Classification with Transformers
Video Classification with Transformers
Point Cloud Classification PointNet
Point Cloud Classification PointNet
Point Cloud Segmentation Using PointNet
Point Cloud Segmentation Using PointNet
Tutorial on Neural Radiance Fields (NeRFs)
Introduction Neural Radiance Fields
3D Vision and Lidar
Introduction to 3D Vision
Introduction to LIDAR Technology
3D Reconstrution
Tutorial on 3D Reconstruction
Computer Vision Anotation Strategies
Best Practices
Medical Project - X-Ray Pneumonia Prediction
X-Ray Pneumonia Prediction
Medical Project - 3D CT Scan Classification
3D CT Scan Classification
Low Light Image Enhancement MIRNet
Low Light Image Enhancement MIRNet
Deploy your CV App using Flask RestFUL API & Web App
Flask RestFUL API
Flask Web App
OCR Captcha Cracker
OCR Captcha Cracker
Productionising Computer Vision Models Cloud, GPUs, Embedded Devices and Mobile
Introduction to Productionising Models
Deploying on the Cloud
Deploying on Embedded Devices
Deploying on GPUs using NVIDIA's DeepStream
Deploying on Mobile Devices
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