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

Master Modern Computer Vision with TensorFlow, Keras & PyTorch (2024 Update)

Unlock the power of Computer Vision and Deep Learning in this comprehensive 2024 course! Dive into the exciting world of AI and learn how computers can "see" and understand the world around them. This in-depth curriculum covers the latest advancements and practical applications, with a focus on hands-on coding using PyTorch and TensorFlow Keras.

What is Computer Vision? Discover the transformative potential of Computer Vision, impacting industries like medical imaging, self-driving cars, security, farming, and manufacturing. The demand for skilled Computer Vision professionals is booming, with salaries often exceeding $200,000! This course provides the essential foundation to thrive in this rapidly growing field.

Why Choose This Course?

• Up-to-Date Curriculum: Covering the most relevant and cutting-edge topics in Computer Vision.
• Hands-on Learning: Taught using accessible Google Colab Notebooks – no complex installations required.
• Dual Framework Expertise: Master both PyTorch and TensorFlow Keras.
• Extensive Content: Over 27+ hours of theory and practical code examples.
• Beginner-Friendly: Designed for those new to Computer Vision, building from fundamental concepts.

Core Concepts & Techniques:

• Classical Computer Vision (OpenCV):
  • Image Operations and Manipulations
  • Contours and Segmentation
  • Simple Object Detection and Tracking
  • Facial Landmarks, Recognition, and Face Swaps
  • Neural Style Transfer, YOLOv3, SSDs, and Image Colorization
  • Working with Video and Streams
• Deep Learning for Computer Vision:
  • Convolutional Neural Networks (CNNs): In-depth analysis, visualization, advanced techniques, transfer learning, and more.
  • Generative Adversarial Networks (GANs): Explore CycleGAN, ArcaneGAN, SuperResolution, and StyleGAN for creative image generation.
  • Autoencoders & Neural Style Transfer: Understand and implement these powerful techniques.
  • Modern CNN Architectures: Learn about ResNets, DenseNets, MobileNET, VGG, Inception, EfficientNet, and Vision Transformers (ViTs).
  • Siamese Networks: For image similarity and recognition.
  • Facial Recognition: Analyze age, gender, emotion, and ethnicity.
  • Object Detection: Master YOLOv5, YOLOv4, EfficientDet, SSDs, Faster R-CNNs, and Deep Segmentation models like MaskCNN, U-NET, and DeepLabV3.
  • Tracking: Implement DeepSORT for robust object tracking.
  • Deepfake Generation & Detection
  • Optical Character Recognition (OCR)
  • Image Captioning
  • 3D Computer Vision: Utilizing Point Cloud Data for analysis.
  • Medical Imaging: Explore X-Ray and CT-Scan analysis.
  • Depth Estimation
  • Building a Computer Vision API with Flask

Exciting Projects to Build Your Portfolio:

• Classical Computer Vision:
  • Shape matching and sorting contours
  • Perspective transforms (CamScanner functionality)
  • Image similarity using K-Means clustering
  • Motion tracking with MeanShift and CAMShift
  • Optical Flow analysis
  • Facial landmark detection and swaps
  • QR Code and Barcode reading
  • Background removal
  • Text detection and OCR
  • Colorizing black and white photos
  • Computational photography techniques
  • Edge detection for sketching
  • Capturing video from RTSP and YouTube
• Deep Learning Computer Vision:
  • PyTorch & Keras CNN Tutorials: From MNIST to Fashion-MNIST and beyond.
  • CNN Visualization: Understanding filters and activations.
  • Transfer Learning: Leveraging pretrained models like VGG16, ResNet, Inception, MobileNet, and EfficientNet.
  • Generative Adversarial Networks (GANs): Create anime with style, turn horses into zebras, and more.
  • Siamese Networks: Implement facial recognition and similarity.
  • Object Detection: Detect guns, masks, sign language, potholes, mushrooms, website regions, drones, chess pieces, and more using various models like YOLOv4, EfficientDet, and Detectron2.
  • Image Segmentation: Utilize U-Net, SegNet, and DeepLabV3.
  • DeepFakes & Vision Transformers: Explore the latest advancements in video manipulation and classification.
  • Image Similarity Search: Implement metric learning for finding similar images