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The Complete Self-Driving Car Course – Applied Deep Learning

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The Complete Self-Driving Car Course – Applied Deep Learning, available at $109.99, has an average rating of 4.59, with 170 lectures, based on 4058 reviews, and has 24880 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone with an interest in Deep Learning and Self Driving Cars or Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence or Entrepreneurs with an interest in working on some of the most cutting edge technologies or All skill levels are welcome! It is particularly useful for Anyone with an interest in Deep Learning and Self Driving Cars or Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence or Entrepreneurs with an interest in working on some of the most cutting edge technologies or All skill levels are welcome!.

Enroll now: The Complete Self-Driving Car Course – Applied Deep Learning

Summary

Title: The Complete Self-Driving Car Course – Applied Deep Learning

Price: $109.99

Average Rating: 4.59

Number of Lectures: 170

Number of Published Lectures: 168

Number of Curriculum Items: 170

Number of Published Curriculum Objects: 168

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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!

Target Audiences

  • 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!

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why This Course?

Chapter 2: Installation

Lecture 1: Overview

Lecture 2: Anaconda Distribution – Mac

Lecture 3: Anaconda Distribution – Windows

Lecture 4: Text Editor

Lecture 5: Outro

Chapter 3: Python Crash Course (Optional)

Lecture 1: Python Crash Course Part 1 – Data Types

Lecture 2: Jupyter Notebooks

Lecture 3: Arithmetic Operations

Lecture 4: Variables

Lecture 5: Numeric Data Types

Lecture 6: String Data Types

Lecture 7: Booleans

Lecture 8: Methods

Lecture 9: Lists

Lecture 10: Slicing

Lecture 11: Membership Operators

Lecture 12: Mutability

Lecture 13: Mutability II

Lecture 14: Common Functions & Methods

Lecture 15: Tuples

Lecture 16: Sets

Lecture 17: Dictionaries

Lecture 18: Compound Data Structures

Lecture 19: Part 1 – Outro

Lecture 20: Part 2 – Control Flow

Lecture 21: If, else

Lecture 22: elif

Lecture 23: Complex Comparisons

Lecture 24: For Loops

Lecture 25: For Loops II

Lecture 26: While Loops

Lecture 27: Break

Lecture 28: Part 2 – Outro

Lecture 29: Part 3 – Functions

Lecture 30: Functions

Lecture 31: Scope

Lecture 32: Doc Strings

Lecture 33: Lambda & Higher Order Functions

Lecture 34: Part 3 – Outro

Chapter 4: NumPy Crash Course (Optional)

Lecture 1: Overview

Lecture 2: Vector Addition – Arrays vs Lists

Lecture 3: Multidimensional Arrays

Lecture 4: One Dimensional Slicing

Lecture 5: Reshaping

Lecture 6: Multidimensional Slicing

Lecture 7: Manipulating Array Shapes

Lecture 8: Matrix Multiplication

Lecture 9: Stacking

Lecture 10: Part 4 – Outro

Chapter 5: Computer Vision: Finding Lane Lines

Lecture 1: Overview

Lecture 2: Image needed for the next lesson

Lecture 3: Loading Image

Lecture 4: Save your file before running!

Lecture 5: Grayscale Conversion

Lecture 6: Smoothening Image

Lecture 7: Simple Edge Detection

Lecture 8: Region of Interest

Lecture 9: Binary Numbers & Bitwise_and

Lecture 10: Line Detection – Hough Transform

Lecture 11: Hough Transform II

Lecture 12: Optimizing

Lecture 13: Resource for upcoming video

Lecture 14: Finding Lanes on Video

Lecture 15: Numpy.float64 Error (Quick Fix)

Lecture 16: Source Code

Lecture 17: Part 5 – Conclusion

Chapter 6: The Perceptron

Lecture 1: Overview

Lecture 2: Machine Learning

Lecture 3: Supervised Learning – Friendly Example

Lecture 4: Classification

Lecture 5: Linear Model

Lecture 6: Perceptrons

Lecture 7: Weights

Lecture 8: Project – Initial Stages

Lecture 9: Sample Code for Initial Stages

Lecture 10: Error Function

Lecture 11: Sigmoid

Lecture 12: Sigmoid Implementation (Code)

Lecture 13: Source code

Lecture 14: Cross Entropy

Lecture 15: Cross Entropy (Code)

Lecture 16: Source Code

Lecture 17: Gradient Descent

Lecture 18: Gradient Descent (Code)

Lecture 19: Recap

Lecture 20: Source Code

Lecture 21: Part 6 – Conclusion

Chapter 7: Keras

Lecture 1: Overview

Lecture 2: Intro to Keras (See next article for installation fix)

Lecture 3: Stop Using Jupyter – Use Colab instead

Lecture 4: How to Import Keras

Lecture 5: Starter Code

Instructors

Rating Distribution

  • 1 stars: 33 votes
  • 2 stars: 43 votes
  • 3 stars: 277 votes
  • 4 stars: 1326 votes
  • 5 stars: 2379 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


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!

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