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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

Description:

Dive into a comprehensive 3.5-hour course on computer vision, exploring machine learning fundamentals, neural networks, and advanced architectures. Learn about supervised and unsupervised learning, data processing techniques, and model evaluation. Discover key computer vision applications and image representation methods. Explore essential neural network components, including neurons, activation functions, and convolutions. Examine popular architectures like LeNet, AlexNet, VGG, and ResNet. Delve into object detection techniques, including bounding boxes, anchor boxes, and models such as R-CNN and YOLO. Investigate semantic segmentation and fully convolutional networks, culminating with the U-Net architecture. Gain hands-on experience using Jupyter Notebooks on SageMaker to reinforce your learning.

Computer Vision

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#Computer Science #Artificial Intelligence #Computer Vision #Machine Learning #Supervised Learning #Unsupervised Learning #Neural Networks #Data Science #Jupyter Notebooks #Data Processing #Model Evaluation

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