This course on Computer Vision aims to teach learners the fundamentals of computer vision, machine learning, and neural networks. By the end of the course, students will be able to understand and apply supervised and unsupervised learning techniques, process imbalanced data, evaluate models, work with different image representations, and implement various convolutional neural network architectures like LeNet, AlexNet, VGG, ResNet, YOLO, and U-Net. The course utilizes a hands-on approach using Jupyter Notebooks on Sagemaker, making it suitable for individuals interested in computer vision, machine learning, and neural networks.
Overview
Syllabus
Accelerated Computer Vision 1.1 - Intro.
Accelerated Computer Vision 1.2 - Introduction to Machine Learning.
Accelerated Computer Vision 1.3 - ML Applications.
Accelerated Computer Vision 1.4 - Supervised and Unsupervised Learning.
Accelerated Computer Vision 1.5 - Data Processing - Imbalanced Data.
Accelerated Computer Vision 1.6 - Underfitting, Overfitting and Model Evaluation.
Accelerated Computer Vision 1.7 - Computer Vision Applications.
Accelerated Computer Vision 1.8 - Image Representation.
Accelerated Computer Vision 1.9 - Neuron & Activation Functions.
Accelerated Computer Vision 1.10 - Neural Networks: Components and Training.
Accelerated Computer Vision 1.11 - Convolutions (Filters).
Accelerated Computer Vision 1.12 - Padding, Stride and Pooling.
Using Jupyter Notebooks on Sagemaker.
Accelerated Computer Vision 2.1 - Computer Vision Datasets.
Accelerated Computer Vision 2.2 - LeNet.
Accelerated Computer Vision 2.3 - AlexNet.
Accelerated Computer Vision 2.4 - Transfer Learning.
Accelerated Computer Vision 3.1 - VGG and Batch Normalization.
Accelerated Computer Vision 3.2 - ResNet.
Accelerated Computer Vision 3.3 - Object Detection Applications.
Accelerated Computer Vision 3.4 - Bounding Box and Anchor Box.
Accelerated Computer Vision 3.5 - Sliding Window Method and Non-max Suppression.
Accelerated Computer Vision 3.6 - Region Based Convolutional Neural Networks (R-CNNs).
Accelerated Computer Vision 3.9 - Fully Convolutional Networks.
Accelerated Computer Vision 3.7 - You Only Look Once (YOLO) model.
Accelerated Computer Vision 3.8 - Semantic Segmentation.
Accelerated Computer Vision 3.10 - U-Net.
MLU Channel Introduction.
Taught by
Machine Learning University
