Completed
Using Jupyter Notebooks on Sagemaker
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Computer Vision
Automatically move to the next video in the Classroom when playback concludes
- 1 Accelerated Computer Vision 1.1 - Intro
- 2 Accelerated Computer Vision 1.2 - Introduction to Machine Learning
- 3 Accelerated Computer Vision 1.3 - ML Applications
- 4 Accelerated Computer Vision 1.4 - Supervised and Unsupervised Learning
- 5 Accelerated Computer Vision 1.5 - Data Processing - Imbalanced Data
- 6 Accelerated Computer Vision 1.6 - Underfitting, Overfitting and Model Evaluation
- 7 Accelerated Computer Vision 1.7 - Computer Vision Applications
- 8 Accelerated Computer Vision 1.8 - Image Representation
- 9 Accelerated Computer Vision 1.9 - Neuron & Activation Functions
- 10 Accelerated Computer Vision 1.10 - Neural Networks: Components and Training
- 11 Accelerated Computer Vision 1.11 - Convolutions (Filters)
- 12 Accelerated Computer Vision 1.12 - Padding, Stride and Pooling
- 13 Using Jupyter Notebooks on Sagemaker
- 14 Accelerated Computer Vision 2.1 - Computer Vision Datasets
- 15 Accelerated Computer Vision 2.2 - LeNet
- 16 Accelerated Computer Vision 2.3 - AlexNet
- 17 Accelerated Computer Vision 2.4 - Transfer Learning
- 18 Accelerated Computer Vision 3.1 - VGG and Batch Normalization
- 19 Accelerated Computer Vision 3.2 - ResNet
- 20 Accelerated Computer Vision 3.3 - Object Detection Applications
- 21 Accelerated Computer Vision 3.4 - Bounding Box and Anchor Box
- 22 Accelerated Computer Vision 3.5 - Sliding Window Method and Non-max Suppression
- 23 Accelerated Computer Vision 3.6 - Region Based Convolutional Neural Networks (R-CNNs)
- 24 Accelerated Computer Vision 3.9 - Fully Convolutional Networks
- 25 Accelerated Computer Vision 3.7 - You Only Look Once (YOLO) model
- 26 Accelerated Computer Vision 3.8 - Semantic Segmentation
- 27 Accelerated Computer Vision 3.10 - U-Net
- 28 MLU Channel Introduction
