The Complete Self-Driving Car Course - Applied Deep Learning
4.6 (2,382 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,859 students enrolled

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
Bestseller
4.6 (2,381 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,859 students enrolled
Last updated 4/2020
English
English [Auto-generated], French [Auto-generated], 2 more
  • German [Auto-generated]
  • Indonesian [Auto-generated]
Current price: $129.99 Original price: $199.99 Discount: 35% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 18 hours on-demand video
  • 26 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • 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
Course content
Expand all 163 lectures 18:06:25
+ Python Crash Course (Optional)
34 lectures 02:10:04
Python Crash Course Part 1 - Data Types
01:05
Jupyter Notebooks
01:39
Arithmetic Operations
04:23
Variables
05:05
Numeric Data Types
04:09
String Data Types
05:45
Booleans
04:27
Methods
03:04
Lists
05:31
Slicing
08:16
Membership Operators
02:50
Mutability
04:08
Mutability II
04:45
Common Functions & Methods
07:32
Tuples
03:32
Sets
02:58
Dictionaries
05:19
Compound Data Structures
02:49
Part 1 - Outro
00:14
Part 2 - Control Flow
00:47
If, else
04:47
elif
06:53
Complex Comparisons
05:11
For Loops
07:17
For Loops II
03:07
While Loops
03:07
Break
03:23
Part 2 - Outro
00:17
Part 3 - Functions
00:51
Functions
05:35
Scope
01:45
Doc Strings
02:45
Lambda & Higher Order Functions
06:07
Part 3 - Outro
00:41
+ NumPy Crash Course (Optional)
10 lectures 01:05:48
Overview
00:48
Vector Addition - Arrays vs Lists
12:03
Multidimensional Arrays
11:46
One Dimensional Slicing
03:33
Reshaping
03:34
Multidimensional Slicing
07:20
Manipulating Array Shapes
08:17
Matrix Multiplication
04:19
Stacking
14:00
Part 4 - Outro
00:08
+ Computer Vision: Finding Lane Lines
16 lectures 01:22:20
Overview
00:35
Image needed for the next lesson
00:02
Grayscale Conversion
04:31
Smoothening Image
03:04
Simple Edge Detection
04:21
Region of Interest
07:41
Binary Numbers & Bitwise_and
09:44
Line Detection - Hough Transform
10:54
Hough Transform II
13:25
Optimizing
14:45
Resource for upcoming video
00:04
Finding Lanes on Video
06:20
Numpy.float64 Error (Quick Fix)
00:21
Source Code
00:58
Part 5 - Conclusion
00:34
+ The Perceptron
21 lectures 01:31:08
Overview
01:44
Supervised Learning - Friendly Example
04:25
Classification
07:48
Linear Model
06:52
Perceptrons
04:08
Weights
02:03
Project - Initial Stages
10:57
Sample Code for Initial Stages
00:10
Error Function
03:36
Sigmoid
05:56
Sigmoid Implementation (Code)
11:46
Source code
00:16
Cross Entropy
05:38
Cross Entropy (Code)
07:41
Source Code
00:19
Gradient Descent
03:14
Gradient Descent (Code)
08:45
Recap
01:54
Source Code
00:25
Part 6 - Conclusion
00:39
+ Keras
9 lectures 44:55
Overview
00:30
Intro to Keras (See next article for installation fix)
02:04
Google Colab
00:28
How to Import Keras
00:15
Starter Code
00:09
Keras Models
21:08
Keras - Predictions
19:25
Source Code
00:33
Part 7 - Outro
00:21
+ Deep Neural Networks
9 lectures 59:03
Overview
00:52
Non-Linear Boundaries
05:05
Architecture
09:00
Feedforward Process
07:46
Error Function
04:10
Backpropagation
05:12
Code Implementation
26:02
Source Code
00:33
Section 8 - Conclusion
00:23
+ Multiclass Classification
6 lectures 52:41
Overview
00:35
Softmax
11:50
Cross Entropy
08:16
Implementation
30:55
Source Code
00:47
Section 9 - Outro
00:18
+ MNIST Image Recognition
10 lectures 01:34:01
Overview
00:48
MNIST Dataset
05:25
Train & Test
13:28
Hyperparameters
07:04
Implementation Part 1
33:45
Implementation Part 2
20:13
Resource for upcoming video
00:02
Implementation Part 3
11:49
Final Source Code
01:03
Section 10 - Outro
00:24
Requirements
  • A working computer
  • No experience required!
Description

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.

Who this course is for:
  • Anyone with an interest in Deep Learning and Self Driving Cars
  • Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence
  • Entrepreneurs with an interest in working on some of the most cutting edge technologies
  • All skill levels are welcome!