Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!
Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!
Next-Gen Computer Vision: YOLOv8, DINO-GPT4V, OpenCV4, Face Recognition, GenerativeAI, Diffusion Models & Transformers
Created by Rajeev D. Ratan | 27.5 hours on-demand video course
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!
- 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 There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations.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!
Recommended Course
Deep Learning and Computer Vision A-Z + AI & ChatGPT Bonuses
YOLOv7 YOLOv8 YOLOv9 YOLOv10 YOLOv11 – Deep Learning Course
What you’ll learn in Computer Vision Course
- All major Computer Vision theory and concepts (updated in late 2024!)
- 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 this Computer Vision course is for:
- 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
