Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!
Next-Gen Computer Vision: YOLOv8, DINO-GPT4V, OpenCV4, Face Recognition, GenerativeAI, Diffusion Models & Transformers
4.05 (1700 reviews)
14 185
students
28.5 hours
content
Nov 2024
last update
$79.99
regular price
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
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Comidoc Review
Our Verdict
The Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024 course offers extensive coverage of classical and contemporary computer vision techniques. While the instructor effectively communicates complex topics, hands-on examples sometimes lack detailed explanations or have outdated code snippets. The course is still a valuable resource for learners with solid Python skills looking to study cutting-edge computer vision methods.
What We Liked
- Instructor has deep knowledge of Computer Vision, providing comprehensive coverage from fundamental to advanced topics.
- Hands-on approach with Python code examples on Google Colab ensures practical experience.
- Well-explained classical and modern concepts; includes YOLOv8, DINO-GPT4V, OpenCV4, Face Recognition, GenerativeAI, Diffusion Models, Transformers, and more.
Potential Drawbacks
- Lacks in-depth explanations of certain parameters, principles, and algorithms.
- Some code examples are outdated or contain errors; instructor may not promptly answer questions.
- Occasionally rushes through code implementation without thorough explanation; assumes foundational Python knowledge
3214523
udemy ID
08/06/2020
course created date
27/02/2022
course indexed date
Ignacio Castro
course submited by