Mix the facility of Machine Studying, Deep Studying and Pc Imaginative and prescient to make a Self-Driving Automobile!
What you’ll be taught
Grasp Machine Studying and Python
Discover ways 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 automobile drive like a human (Behavioural Cloning)
Simulate a Self-Driving automobile in a sensible surroundings utilizing a number of strategies (Pc Imaginative and prescient, Convolution Neural Networks, …)
Create robust added worth to your corporation
Mild introduction to Machine Studying the place all the important thing ideas are introduced in an intuitive means
Code Deep Convolutional Neural Networks with Keras (the preferred library)
Study to use Pc Imaginative and prescient and Deep Studying strategies to construct automotive associated algorithms
Perceive how Self Driving Automobiles work (sensors, actuators, pace management, …)
Study to code in Python ranging from the very starting
Python libraires: NumPy, Sklearn (Scikit-Study), Keras, OpenCV, Matplotlib
Description
Considering Machine Studying or Self-Driving Automobiles (i.e. Tesla)? Then this course is for you!
This course has been designed by knowledgeable Knowledge Scientist, skilled in Autonomous Automobiles, with the aim of sharing my information and assist you perceive how Self-Driving Automobiles work in a easy means.
Every subject is introduced at three ranges:
- Introduction: the subject can be introduced, preliminary instinct about it
- Arms-On: sensible lectures the place we’ll be taught by doing
- [Optional] Deep dive: going deep into the maths to completely perceive the subject
What instruments will we use within the course?
- Python: most likely probably the most versatile programming language on the planet, from web sites to Deep Neural Networks, all might be performed 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 situations (Self-Driving Automobiles, 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 can educate you methods to program in Python
- Maths/logic: Excessive-school stage is sufficient to perceive every thing!
Sections:
- [Optional] Python sections: Tips on how to program in python, and methods to use important libraries
- Pc Imaginative and prescient: teaches a pc methods to see, and introduces key ideas for Neural Networks
- Machine Studying: introduction, key ideas, and highway signal classification
- Collision Avoidance: thus far 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 automobile 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 Concept: management programs is the glue that stitches all engineering fields collectively
- If you’re primarily fascinated with ML, you may solely hearken to the introduction for this part, however you need to know that the preliminary Neural Networks had 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 the planet
- Over 8 years expertise within the trade and a grasp in Robotic & CV
- All the time been fascinated with environment friendly studying, and used all of the strategies that I’ve realized on this course
English
language
Content material
Introduction
Why This Course?
Tips on how to Method This Course?
Make it Participating
Get the Course Code for Sensible Lectures
Python [Optional]
Set up
Varieties 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
Pc Imaginative and prescient
Introduction to Pc Imaginative and prescient
How Computer systems “See” Photographs?
Kernel & Convolution
Picture Processing with Kernels
Thresholding
Highway Segmentation
Why Webots?
Tips on how to Set up Webots in Home windows?
Tips on how to Set up Webots in Linux?
Webots too sluggish?
Webots Code: Defined
[Exercise]: Your Line Following Algorithm!
[Advanced] Tips on how to Learn a Paper?
[Advanced] Paper: SIFT
Machine Studying
What’s Machine Studying?
Practice, Predict & Consider
Kinds of Machine Studying
ML for Self-Driving Automobiles
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 Study?
Convolutional Neural Networks
Code Instance
Deep Studying: Arms-On
Deep Studying Arms-On: Introduction
Making a Dataset
Coaching
See it drive!
[Exercise]: Practice it your self!
[Advanced] AlexNet
Management Concept
Why Study Management Concept
Management Programs 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 – Tips on how to Tune it?
PID Controller – Why is it use SO a lot?
[Advanced] Paper: PID Controller Design
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