Udacity – Computer Vision Nanodegree nd891 v1.0.0

Udacity – Computer Vision Nanodegree nd891 v1.0.0
English | Size: 2.66 GB
Category: Tutorial

Computer Vision is a specialized branch of Artificial Intelligence focused on enabling machines to visually perceive the world, and respond to it. As with human vision, this is a process of taking in visual information, analyzing and processing that information, and correctly identifying objects contained within that information.
Thanks to advances in the field of Computer Vision – and significant increases in available computing power – machines can now “see” thousands and thousands of images, and process them far more rapidly and accurately than a human could ever do.

Our new Computer Vision Nanodegree program covers all the latest techniques. You’ll learn about deep learning architectures like R-CNN and YOLO (You Only Look Once) multi-object recognition models, and you’ll implement object tracking methods like SLAM (Simultaneous Localization and Mapping).

Content:

Part 01-Module 01-Lesson 01_Welcome to Computer Vision
Part 01-Module 01-Lesson 02_Image Representation & Classification
Part 01-Module 01-Lesson 03_Convolutional Filters and Edge Detection
Part 01-Module 01-Lesson 04_Types of Features & Image Segmentation
Part 01-Module 01-Lesson 05_Feature Vectors
Part 01-Module 01-Lesson 06_CNN Layers and Feature Visualization
Part 01-Module 01-Lesson 07_Project Facial Keypoint Detection
Part 02-Module 01-Lesson 01_Advanced CNN Architectures
Part 02-Module 01-Lesson 02_YOLO
Part 02-Module 01-Lesson 03_RNN’s
Part 02-Module 01-Lesson 04_ Long Short-Term Memory Networks (LSTMs)
Part 02-Module 01-Lesson 05_Hyperparameters
Part 02-Module 01-Lesson 06_Optional Attention Mechanisms
Part 02-Module 01-Lesson 07_Image Captioning
Part 02-Module 01-Lesson 08_Project Image Captioning
Part 02-Module 01-Lesson 09_Optional Cloud Computing with AWS
Part 03-Module 01-Lesson 01_Introduction to Motion
Part 03-Module 01-Lesson 02_Robot Localization
Part 03-Module 01-Lesson 03_Mini-project 2D Histogram Filter
Part 03-Module 01-Lesson 04_Introduction to Kalman Filters
Part 03-Module 01-Lesson 05_Representing State and Motion
Part 03-Module 01-Lesson 06_Matrices and Transformation of State
Part 03-Module 01-Lesson 07_Simultaneous Localization and Mapping
Part 03-Module 01-Lesson 08_Optional Vehicle Motion and Calculus
Part 03-Module 01-Lesson 09_Project Landmark Detection & Tracking (SLAM)
Part 04-Module 01-Lesson 01_Applying Deep Learning Models
Part 05-Module 01-Lesson 01_Feedforward and Backpropagation
Part 05-Module 01-Lesson 02_Training Neural Networks
Part 05-Module 01-Lesson 03_Deep Learning with PyTorch
Part 06-Module 01-Lesson 01_Deep Learning for Cancer Detection with Sebastian Thrun
Part 07-Module 01-Lesson 01_Sentiment Analysis
Part 08-Module 01-Lesson 01_Fully-Convolutional Neural Networks & Semantic Segmentation
Part 09-Module 01-Lesson 01_C++ Getting Started
Part 09-Module 01-Lesson 02_C++ Vectors
Part 09-Module 01-Lesson 03_Practical C++
Part 09-Module 01-Lesson 04_C++ Object Oriented Programming
Part 09-Module 01-Lesson 05_Python and C++ Speed
Part 09-Module 02-Lesson 01_C++ Intro to Optimization
Part 09-Module 02-Lesson 02_C++ Optimization Practice
Part 09-Module 02-Lesson 03_Project Optimize Histogram Filter

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