This Computer Vision from Scratch course is designed to give you a strong foundation in both classical image processing and modern deep learning techniques used in computer vision. It is structured as a step-by-step learning series that starts from the basics and gradually moves to advanced neural network concepts.
You will begin by understanding what computer vision is and how machines interpret images. The course then introduces fundamental concepts such as filters, convolution, and image transformations, which are essential building blocks in vision systems.
Next, you will explore neural networks and how they are applied to image classification problems. The course explains how models learn from visual data and how performance can be improved using training techniques.
You will also learn advanced concepts like hyperparameter tuning, experiment tracking, and model optimization using modern tools. The course covers overfitting prevention techniques such as regularization, dropout, early stopping, and batch normalization.
In addition, you will study transfer learning using pretrained models, fine-tuning strategies, and deep architectures like AlexNet and VGGNet, which revolutionized the field of computer vision.
By the end of this course, you will understand how computer vision systems are built from scratch and how deep learning models are trained and optimized for real-world applications.
