Description
Become a Computer Vision Expert is a comprehensive computer vision training course aimed at semi-professional to advanced audiences, published on the popular Udacity Academy. Computer vision is a specialized branch of artificial intelligence that aims to enable machines to visually perceive and respond to the world. Like human vision, it is the process of receiving visual information, analyzing and processing that information, and correctly identifying the objects contained 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 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 the computer vision skills behind advancements in robotics and automation. You will write programs to analyze images, implement feature extraction and object recognition using deep learning models. If you want to watch this course in full and for free, download it now from Downloadly. If you do not have the prerequisites to start this course and you plan to learn this course. You can search for all the required skills on the Downloadly site and learn them too.
Who should attend :
- 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 interested in using computer vision in image processing techniques.
- Beginners and beginners in the field of computer vision science
- Python people interested in learning computer vision
- Intermediate people in the field of working with Python who have skills in problem solving, algorithms and data structure, etc.
- People familiar with machine learning and deep learning.
- Individuals familiar with statistics concepts and topics
- And…
What you will learn in the Become a Computer Vision Expert course:
- Know the basics and requirements of computer vision
- Advanced computer vision and deep learning
- Combine CNN and RNN to create an automatic image capture program
- Tracking objects and people
- Location
- Build a real and concrete project
- And…
Course Specifications Become a Computer Vision Expert:
Editors: audacity
Instructor: Thrun, Cezanne Camacho, Alexis Cook, Juan Delgado, Jay Alammar, Ortal Arel And Luis Serrano
French language
Training level: introduction to advanced
Number of courses: 40
Duration of training: assuming 10 hours of work per week, approximately 3 months
Course Titles Become a Computer Vision Expert:
Part 01: Introduction to Deep Reinforcement Learning
Part 01-Module 01-Lesson 02_Image representation and classification
Part 01-Module 01-Lesson 03_Convolutional filters and edge detection
Part 01-Module 01-Lesson 04_Feature Types and 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 Detection of key points of the face
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-term memory networks (LSTM)
Part 02-Module 01-Lesson 05_Hyperparameters
Part 02-Module 01-Lesson 06_Optional attention mechanisms
Part 02-Module 01-Lesson 07_Image subtitling
Part 02-Module 01-Lesson 08_Project Image Subtitling
Part 02-Module 01-Lesson 09_Optional Cloud Computing with AWS
Part 03-Module 01-Lesson 01_Introduction to movement
Part 03-Module 01-Lesson 02_Location of the robot
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_Representation of state and movement
Part 03-Module 01-Lesson 06_Matrices and transformation of the State
Part 03-Module 01-Lesson 07_Simultaneous localization and mapping
Part 03-Module 01-Lesson 08_Optional vehicle movement and calculation
Part 03-Module 01-Lesson 09_Detection and monitoring of project benchmarks (SLAM)
Part 04-Module 01-Lesson 01_Application of deep learning models
Part 05-Module 01-Lesson 01_Feedforward and backpropagation
Part 05-Module 01-Lesson 02_Training of 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 and 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_Speed Python and C++
Part 09-Module 02-Lesson 01_C++ Introduction to optimization
Part 09-Module 02-Lesson 02_C++ Optimization Practice
Part 09-Module 02-Lesson 03_Project Optimize the histogram filter
Requirements:
- Intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and read and understand code written by others.
- Context of intermediate statistics. You know the odds.
- Intermediate knowledge of machine learning techniques. You may describe backpropagation and have seen some examples of neural network architecture (like a CNN for image classification).
- You have already seen or worked with a deep learning framework like TensorFlow, Keras or PyTorch.
Pictures
Introduction and introductory video of the Computer Vision Expert course:
installation guide
After extract, viewing with the desired player.
english subtitles
Quality: 720p
Download link
Password of the file(s): www.downloadly.ir
size
2.60 GB