Complete Computer Vision Bootcamp With PyTorch & Tensorflow

In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNNs) 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 (CNNs)Introduction to CNNs architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNNs 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 Res Net, VGG, and Efficient Net.Fine-tuning and optimizing transfer learning models.Object Detection Models Exploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNs Implement