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30-Day Money-Back Guarantee

This course includes:

  • 18 hours on-demand video
  • 32 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Deep Learning

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
Rating: 4.5 out of 54.5 (2,980 ratings)
16,937 students
Created by Rayan Slim, Amer Sharaf, Jad Slim, Sarmad Tanveer
Last updated 9/2020
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

14 sections • 170 lectures • 18h 7m total length

  • Preview02:25
  • Additional FREE Content
    00:10

  • Preview00:24
  • Preview02:41
  • Preview02:54
  • Text Editor
    02:47
  • Outro
    00:29

  • Python Crash Course (Written Version) - Free Access
    00:55
  • Python Crash Course (Written Version)
    00:19
  • 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

  • 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

  • Overview
    00:35
  • Image needed for the next lesson
    00:02
  • Preview05:00
  • Save your file before running!
    00:08
  • 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

  • Overview
    01:44
  • Preview02:51
  • 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

  • Overview
    00:30
  • Intro to Keras (See next article for installation fix)
    02:04
  • Stop Using Jupyter - Use Colab instead
    00:20
  • How to Import Keras
    00:15
  • Starter Code
    00:09
  • Fix for next video (update)
    00:02
  • Keras Models
    21:08
  • Keras - Predictions
    19:25
  • Source Code
    00:33
  • Part 7 - Outro
    00:21

  • Overview
    00:52
  • Non-Linear Boundaries
    05:05
  • Architecture
    09:00
  • Feedforward Process
    07:46
  • Error Function
    04:10
  • Backpropagation
    05:12
  • Fix for next video (update)
    00:02
  • Code Implementation
    26:02
  • Source Code
    00:32
  • Section 8 - Conclusion
    00:23

  • Overview
    00:35
  • Softmax
    11:50
  • Cross Entropy
    08:16
  • Implementation
    30:55
  • Source Code
    00:47
  • Section 9 - Outro
    00:18

  • Overview
    00:48
  • MNIST Dataset
    05:25
  • Train & Test
    13:28
  • Hyperparameters
    07:04
  • Implementation Part 1
    33:45
  • Fix for upcoming video
    00:02
  • 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!

Featured review

Spyros Nikolakis
Spyros Nikolakis
59 courses
3 reviews
Rating: 5.0 out of 510 months ago
This course was an excellent introduction to deep learning for self-driving cars. I loved the explanations and especially the last project was pretty neat. I will certainly look to the instructor's other courses as well. Thank you!

Instructors

Rayan Slim
Teacher
Rayan Slim
  • 4.5 Instructor Rating
  • 11,521 Reviews
  • 107,562 Students
  • 6 Courses

Hi! I'm Rayan, a full time software developer based in Ottawa, Canada.

I first ventured into development when working on a start-up. Since then, I've built countless web and mobile applications as a freelance developer, meanwhile expanding my repertoire and exploring new avenues in Deep Learning & Data Analytics.

In my free time, I love to teach!

Amer Sharaf
Developer
Amer Sharaf
  • 4.5 Instructor Rating
  • 4,303 Reviews
  • 23,370 Students
  • 2 Courses

   Hi I'm Amer. I'm a full-time developer with a specialized interest in Artificial intelligence (AI).  AI is now taking on more sophisticated roles that can truly amplify human capabilities.

   With a background in Mechanical Engineering and computer science I have always looked for ways to use the power of AI to create practical solutions that revolutionize the way we live.

I aim to make artificial intelligence more accessible to all students, no matter the skill level! 

Jad Slim
Developer
Jad Slim
  • 4.5 Instructor Rating
  • 11,521 Reviews
  • 96,054 Students
  • 6 Courses

Jad studied mechanical engineering at the University of Ottawa. Jad also has experience in software development, machine learning, computer vision, mathematical modeling, computer simulation, and intelligent systems. He has also developed many deep learning applications, and is currently pursuing an interest in autonomous machines and Full Stack Development.


Sarmad Tanveer
Data Scientist
Sarmad Tanveer
  • 4.5 Instructor Rating
  • 7,070 Reviews
  • 37,526 Students
  • 3 Courses

Hi! I'm Sarmad, and I have graduate level expertise in Mechanical Engineering. My main areas of interest and research include autonomous robotics, self driving car technology and machine learning.

In my spare time, I enjoy teaching courses on Udemy and sharing my knowledge with all of you!



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