Mix the facility of Machine Studying, Deep Studying and Laptop Imaginative and prescient to make a Self-Driving Automobile!
What you’ll study
Grasp Machine Studying and Python
Learn to apply Machine Studying algorithms to develop a Self-Driving Automobile from scratch
Perceive why Deep Studying is such a revolution and use it to make the automotive drive like a human (Behavioural Cloning)
Simulate a Self-Driving automotive in a practical atmosphere utilizing a number of strategies (Laptop Imaginative and prescient, Convolution Neural Networks, …)
Create robust added worth to your online business
Mild introduction to Machine Studying the place all the important thing ideas are introduced in an intuitive manner
Code Deep Convolutional Neural Networks with Keras (the preferred library)
Be taught to use Laptop Imaginative and prescient and Deep Studying strategies to construct automotive associated algorithms
Perceive how Self Driving Vehicles work (sensors, actuators, pace management, …)
Be taught to code in Python ranging from the very starting
Python libraires: NumPy, Sklearn (Scikit-Be taught), Keras, OpenCV, Matplotlib
Description
Involved in Machine Studying or Self-Driving Vehicles (i.e. Tesla)? Then this course is for you!
This course has been designed by knowledgeable Knowledge Scientist, skilled in Autonomous Autos, with the purpose of sharing my information and provide help to perceive how Self-Driving Vehicles work in a easy manner.
Every matter is introduced at three ranges:
- Introduction: the subject shall be introduced, preliminary instinct about it
- Arms-On: sensible lectures the place we’ll study by doing
- [Optional] Deep dive: going deep into the maths to totally perceive the subject
What instruments will we use within the course?
- Python: in all probability probably the most versatile programming language on this planet, from web sites to Deep Neural Networks, all could be carried out in Python
- Python libraries: matplotlib, OpenCV, numpy, scikit-learn, keras, … (these libraries make the probabilities of Python limitless)
- Webots: a really highly effective simulator, which free and open supply however can present a variety of simulation eventualities (Self-Driving Vehicles, drones, quadrupeds, robotic arms, manufacturing traces, …)
Who this course is for?
- All-levels: there is no such thing as a earlier information required, there’s a part that may educate you easy methods to program in Python
- Maths/logic: Excessive-school stage is sufficient to perceive every thing!
Sections:
- [Optional] Python sections: Learn how to program in python, and easy methods to use important libraries
- Laptop Imaginative and prescient: teaches a pc easy methods to see, and introduces key ideas for Neural Networks
- Machine Studying: introduction, key ideas, and street signal classification
- Collision Avoidance: up to now we have now used cameras, on this part we perceive how radar and lidar sensors are used for self-driving vehicles, use them for collision avoidance, path planning
- Assist us perceive the distinction between Tesla and different automotive producers, as a result of Tesla doesn’t use radar sensors
- Deep studying: we’ll use all of the ideas that we have now seen earlier than in CV, in ML and CA, neural networks introduction, Behavioural Cloning
- Management Idea: management techniques is the glue that stitches all engineering fields collectively
- If you’re primarily fascinated with ML, you’ll be able to solely take heed to the introduction for this part, however it’s best to know that the preliminary Neural Networks have been closely influenced by CT
Who am I, and why am I certified to speak about Self-driving vehicles?
- Labored in self-driving motorbikes, boats and vehicles
- A few of the greatest corporations on this planet
- Over 8 years expertise within the trade and a grasp in Robotic & CV
- At all times been fascinated with environment friendly studying, and used all of the strategies that I’ve discovered on this course
English
language
Content material
Introduction
Why This Course?
Learn how to Strategy This Course?
Make it Partaking
Get the Course Code for Sensible Lectures
Python [Optional]
Set up
Sorts in Python
Record & Map
Operations
Statements
Features
Object Oriented Programming
Lessons
Libraries / Modules
Python’s Important Libraries
Introduction to Python Libraries
Numpy
Matplotlib
OpenCV
Different Libraries
Laptop Imaginative and prescient
Introduction to Laptop Imaginative and prescient
How Computer systems “See” Photos?
Kernel & Convolution
Picture Processing with Kernels
Thresholding
Street Segmentation
Why Webots?
Learn how to Set up Webots in Home windows?
Learn how to Set up Webots in Linux?
Webots too sluggish?
Webots Code: Defined
[Exercise]: Your Line Following Algorithm!
[Advanced] Learn how to Learn a Paper?
[Advanced] Paper: SIFT
Machine Studying
What’s Machine Studying?
Prepare, Predict & Consider
Sorts of Machine Studying
ML for Self-Driving Vehicles
Machine Studying Arms-On
Machine Studying Arms-On: Introduction
Characteristic Engineering
HOG
SVM
Efficiency Metrics
Obtain the Dataset
Code Clarification
[Exercise]: Modify the code
Helpful ML Fashions
Bias Vs Variance
[Advanced] Paper: SVM
Collision Avoidance
Collision Avoidance: Introduction
Ranging Sensors
Cameras
Simulation
My Resolution
[Exercise]: Your Resolution
Path Planning
[Advanced] RRT Code
Deep Studying
Deep Studying: Introduction
How do Neural Networks Work?
How does a Neural Community Be taught?
Convolutional Neural Networks
Code Instance
Deep Studying: Arms-On
Deep Studying Arms-On: Introduction
Making a Dataset
Coaching
See it drive!
[Exercise]: Prepare it your self!
[Advanced] AlexNet
Management Idea
Why Be taught Management Idea
Management Methods Map
Stability – Introduction
Stability – Lacking in Machine Studying
Open and Closed Loop Management
Closed Loop Management – Cruise Management
PID – Introduction
PID Controller – Deep Dive
PID Controller – Learn how to Tune it?
PID Controller – Why is it use SO a lot?
[Advanced] Paper: PID Controller Design
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