Description
Become a Computer Vision Expert is a complete computer vision training course for semi-professional to advanced audiences published on the popular Udacity Academy. Computer vision is a specialized branch of artificial intelligence that focuses on enabling machines to visually perceive the world and respond to it.
Like human vision, it is the process of receiving visual information, analyzing and processing that information, and correctly identifying the objects in that information. Thanks to advances in computer vision – and a significant increase in available computing power – machines can now see thousands and thousands of images and process them much faster and more accurately than a human.
This training course covers the latest and most comprehensive techniques in computer vision. You will learn about deep learning architectures such as R-CNN and YOLO (You Only One Look) multi-object detection models and implement object tracking methods such as SLAM (Simultaneous Localization and Mapping).
You master computer vision skills behind advances in robotics and automation. You will write programs to analyze images, implement feature extraction and object recognition using deep learning models. If you are interested in watching this course completely and for free, download it now from Downloadly. If you do not have the prerequisites to start this course and you intend to learn this course. You can search for all the required skills on the Downloadly site and learn them as well.
Who is this course suitable for:
- Those who want to become a computer vision or deep learning engineer.
- For people who want to improve their machine learning and deep learning skills.
- People who want to use computer vision in image processing techniques.
- Newbies and beginners in the field of computer vision science
- Python people who are interested in learning computer vision
- Intermediate people in the field of working with Python who have problem solving, algorithm and data structure skills, etc.
- People familiar with machine learning and deep learning.
- People familiar with the concepts and topics of statistics
- And …
What you will learn in the Become a Computer Vision Expert course:
- Getting to know the basics and requirements of computer vision
- Advanced computer vision and deep learning
- Combining CNN and RNN to build an automatic image capture program
- Object and person tracking
- Localization
- Building a real and practical project
- And …
Specifications of the Become a Computer Vision Expert course:
Publisher: Udacity
Instructors: Sebastian Thrun , Cezanne Camacho , Alexis Cook , Juan Delgado , Jay Alammar , Ortal Arel and Luis Serrano
Language: English
Education Level: Introductory to Advanced
Number of Lessons: 40
Duration of Education: Assuming 10 hours of work per week approx. 3 months
Titles of the Become a Computer Vision Expert course :
Part 01 : Introduction to Deep Reinforcement Learning
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 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 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
Course prerequisites:
- ntermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
- Intermediate statistics background. You are familiar with probability.
- Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (like a CNN for image classification).
- You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.
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Download part 1 – 1 GB
Download part 2 – 1 GB
Download part 3 – 592 MB
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