Uploaded March 2025 | Updated September 2026, 1 week ago
Enrollment Link With Coupon : udemy.com/course/complete-computer-vision-bootcamp-with-pytoch-tensorflow/?couponCode=MARCH02
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.
What You Will Learn
Throughout this course, you will gain expertise in:
Introduction to Computer Vision
Understanding image data and its structure.
Exploring pixel values, channels, and color spaces.
Learning about OpenCV for image manipulation and preprocessing.
Deep Learning Fundamentals for Computer Vision
Introduction to Neural Networks and Deep Learning concepts.
Understanding backpropagation and gradient descent.
Key concepts like activation functions, loss functions, and optimization techniques.
Convolutional Neural Networks (CNN)
Introduction to CNN architecture and its components.
Understanding convolution layers, pooling layers, and fully connected layers.
Implementing CNN models using TensorFlow and PyTorch.
Data Augmentation and Preprocessing
Techniques for improving model performance through data augmentation.
Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.
Transfer Learning for Computer Vision
Utilizing pre-trained models such as ResNet, VGG, and EfficientNet.
Fine-tuning and optimizing transfer learning models.
Object Detection Models
Exploring object detection algorithms like:
YOLO (You Only Look Once)
SSD (Single Shot MultiBox Detector)
Faster R-CNN
Implementing these models with TensorFlow and PyTorch.
Image Segmentation Techniques
Understanding semantic and instance segmentation.
Implementing U-Net and Mask R-CNN models.
Enrollment Link With Coupon : udemy.com/course/complete-computer-vision-bootcamp-with-pytoch-tensorflow/?couponCode=MARCH02
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.
What You Will Learn
Throughout this course, you will gain expertise in:
Introduction to Computer Vision
Understanding image data and its structure.
Exploring pixel values, channels, and color spaces.
Learning about OpenCV for image manipulation and preprocessing.
Deep Learning Fundamentals for Computer Vision
Introduction to Neural Networks and Deep Learning concepts.
Understanding backpropagation and gradient descent.
Key concepts like activation functions, loss functions, and optimization techniques.
Convolutional Neural Networks (CNN)
Introduction to CNN architecture and its components.
Understanding convolution layers, pooling layers, and fully connected layers.
Implementing CNN models using TensorFlow and PyTorch.
Data Augmentation and Preprocessing
Techniques for improving model performance through data augmentation.
Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.
Transfer Learning for Computer Vision
Utilizing pre-trained models such as ResNet, VGG, and EfficientNet.
Fine-tuning and optimizing transfer learning models.
Object Detection Models
Exploring object detection algorithms like:
YOLO (You Only Look Once)
SSD (Single Shot MultiBox Detector)
Faster R-CNN
Implementing these models with TensorFlow and PyTorch.
Image Segmentation Techniques
Understanding semantic and instance segmentation.
Implementing U-Net and Mask R-CNN models.
