4.62 out of 5
4.62
1098 reviews on Udemy

The Complete Self-Driving Car Course – Applied Deep Learning

Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python
Instructor:
Rayan Slim
7,536 students enrolled
English [Auto-generated] More
Learn to apply Computer Vision and Deep Learning techniques to build automotive-related algorithms
Understand, build and train Convolutional Neural Networks with Keras
Simulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer Vision
Train a Deep Learning Model that can identify between 43 different Traffic Signs
Learn to use essential Computer Vision techniques to identify lane lines on a road
Learn to build and train powerful Neural Networks with Keras
Understand Neural Networks at the most fundamental perceptron-based level

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector. 

Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today.

Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a “learn by doing” style to create this amazing course.

You’ll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.

By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company.

This course will show you how to:

  • Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.

  • Learn to train a Perceptron-based Neural Network to classify between binary classes.

  • Learn to train Convolutional Neural Networks to identify between various traffic signs.

  • Train Deep Neural Networks to fit complex datasets.

  • Master Keras, a power Neural Network library written in Python.

  • Build and train a fully functional self driving car to drive on its own!

No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.

This course also comes with all the source code and friendly support in the Q&A area.

Introduction

1
Why This Course?

Installation

1
Overview
2
Anaconda Distribution - Mac
3
Anaconda Distribution - Windows
4
Text Editor
5
Outro

Python Crash Course (Optional)

1
Python Crash Course Part 1 - Data Types
2
Jupyter Notebooks
3
Arithmetic Operations
4
Variables
5
Numeric Data Types
6
String Data Types
7
Booleans
8
Methods
9
Lists
10
Slicing
11
Membership Operators
12
Mutability
13
Mutability II
14
Common Functions & Methods
15
Tuples
16
Sets
17
Dictionaries
18
Compound Data Structures
19
Part 1 - Outro
20
Part 2 - Control Flow
21
If, else
22
elif
23
Complex Comparisons
24
For Loops
25
For Loops II
26
While Loops
27
Break
28
Part 2 - Outro
29
Part 3 - Functions
30
Functions
31
Scope
32
Doc Strings
33
Lambda & Higher Order Functions
34
Part 3 - Outro

NumPy Crash Course (Optional)

1
Overview
2
Vector Addition - Arrays vs Lists
3
Multidimensional Arrays
4
One Dimensional Slicing
5
Reshaping
6
Multidimensional Slicing
7
Manipulating Array Shapes
8
Matrix Multiplication
9
Stacking
10
Part 4 - Outro

Computer Vision: Finding Lane Lines

1
Overview
2
Image needed for the next lesson
3
Loading Image
4
Grayscale Conversion
5
Smoothening Image
6
Simple Edge Detection
7
Region of Interest
8
Binary Numbers & Bitwise_and
9
Line Detection - Hough Transform
10
Hough Transform II
11
Optimizing
12
Resource for upcoming video
13
Finding Lanes on Video
14
Source Code
15
Part 5 - Conclusion

The Perceptron

1
Overview
2
Machine Learning
3
Supervised Learning - Friendly Example
4
Classification
5
Linear Model
6
Perceptrons
7
Weights
8
Project - Initial Stages
9
Sample Code for Initial Stages
10
Error Function
11
Sigmoid
12
Sigmoid Implementation (Code)
13
Source code
14
Cross Entropy
15
Cross Entropy (Code)
16
Source Code
17
Gradient Descent
18
Gradient Descent (Code)
19
Recap
20
Source Code
21
Part 6 - Conclusion

Keras

1
Overview
2
Intro to Keras
3
Starter Code
4
Keras Models
5
Keras - Predictions
6
Source Code
7
Part 7 - Outro
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.6
4.6 out of 5
1098 Ratings

Detailed Rating

Stars 5
655
Stars 4
357
Stars 3
73
Stars 2
8
Stars 1
6
0cf93ab044bce31598808ee75d0c26df
30-Day Money-Back Guarantee

Includes

18 hours on-demand video
21 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion