The Complete Self-Driving Car Course - Applied Deep Learning from Udemy

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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

Why This Course?

Installation

Overview

Anaconda Distribution - Mac

Anaconda Distribution - Windows

Text Editor

Outro

Python Crash Course (Optional)

Python Crash Course Part 1 - Data Types

Jupyter Notebooks

Arithmetic Operations

Variables

Numeric Data Types

String Data Types

Booleans

Methods

Lists

Slicing

Membership Operators

Mutability

Mutability II

Common Functions & Methods

Tuples

Sets

Dictionaries

Compound Data Structures

Part 1 - Outro

Part 2 - Control Flow

If, else

elif

Complex Comparisons

For Loops

For Loops II

While Loops

Break

Part 2 - Outro

Part 3 - Functions

Functions

Scope

Doc Strings

Lambda & Higher Order Functions

Part 3 - Outro

NumPy Crash Course (Optional)

Vector Addition - Arrays vs Lists

Multidimensional Arrays

One Dimensional Slicing

Reshaping

Multidimensional Slicing

Manipulating Array Shapes

Matrix Multiplication

Stacking

Part 4 - Outro

Computer Vision: Finding Lane Lines

Image needed for the next lesson

Loading Image

Save your file before running!

Grayscale Conversion

Smoothening Image

Simple Edge Detection

Region of Interest

Binary Numbers & Bitwise_and

Line Detection - Hough Transform

Hough Transform II

Optimizing

Resource for upcoming video

Finding Lanes on Video

Numpy.float64 Error (Quick Fix)

Source Code

Part 5 - Conclusion

The Perceptron

Machine Learning

Supervised Learning - Friendly Example

Classification

Linear Model

Perceptrons

Weights

Project - Initial Stages

Sample Code for Initial Stages

Error Function

Sigmoid

Sigmoid Implementation (Code)

Source code

Cross Entropy

Cross Entropy (Code)

Gradient Descent

Gradient Descent (Code)

Recap

Part 6 - Conclusion

Keras

Intro to Keras (See next article for installation fix)

Stop Using Jupyter - Use Colab instead

How to Import Keras

Starter Code

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers fundamental neural networks at a basic perceptron level

Provides instruction to learn and master Deep Learning in a fun and exciting course with top instructor Rayan Slim

Instructs learners how to build and train fully functional self driving car to drive itself

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

Designed for students with no programming or mathematics experience

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Complete Self-Driving Car Course - Applied Deep Learning with these activities:

Review Linear Algebra

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Boost your understanding of linear algebra concepts to enhance your grasp of deep learning algorithms.

Browse courses on Linear Algebra

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Revisit vector spaces, linear transformations, and eigenvalues
Practice solving systems of linear equations and matrix operations
Review concepts related to eigenvectors and diagonalization of matrices

Read 'Deep Learning' by Ian Goodfellow et al.

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Gain a comprehensive understanding of deep learning principles and applications through this authoritative textbook.

View Deep Learning on Amazon

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Read chapters on the foundations and core concepts of deep learning
Explore sections on advanced topics such as convolutional neural networks and recurrent neural networks

Explore Keras Documentation

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Familiarize yourself with the Keras documentation to gain a deeper understanding of its functionality.

Browse courses on Keras

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Navigate the Keras website and explore its documentation
Read tutorials on building and training neural networks with Keras

Three other activities

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Show all six activities

Solve Perceptron Training Problems

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Sharpen your skills in training perceptrons by solving practice problems.

Browse courses on Perceptron

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Set up a coding environment for perceptron training
Implement the perceptron algorithm from scratch
Train perceptrons on various binary classification datasets

Build a Simple Image Classifier

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Solidify your understanding of deep learning by building a simple image classifier from scratch.

Browse courses on Image Classification

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Gather a small dataset of images
Preprocess and label the images
Design and train a convolutional neural network for image classification
Evaluate the performance of your image classifier

Contribute to the TensorFlow Community

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Enhance your deep learning skills and gain practical experience by contributing to an open-source deep learning project.

Browse courses on TensorFlow

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Identify an area in the TensorFlow project where you can contribute
Fork the TensorFlow repository and work on your changes locally
Submit a pull request with your proposed changes

Career center

Learners who complete The Complete Self-Driving Car Course - Applied Deep Learning will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers design and implement deep learning models to solve a variety of business problems. This course would give someone in this role the opportunity to apply deep learning to a real-world problem, namely, self-driving cars.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

Data Scientists create and interpret algorithms, visuals, and dashboards that help organizations make informed decisions. The ability to use Deep Learning, as taught in this course, in the context of automotive related projects would make one an excellent candidate for such a role.

See salaries and explore the career path for Data Scientist

Automotive Engineer

Automotive Engineers typically work for automobile manufacturers or suppliers, and they are responsible for the design, development, testing, and manufacturing of automobiles. This course would help give someone in this role a competitive edge in the area of self-driving cars.

See salaries and explore the career path for Automotive Engineer

Autonomous Systems Specialist

Autonomous Systems Specialists work with hardware, software, and control systems to develop autonomous systems. This course's specialization in Deep Learning as it pertains to self-driving cars may make one a top candidate for a role in this emerging field.

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Machine Learning Engineer

Machine Learning Engineers contribute to the development, implementation, and maintenance of machine learning algorithms and infrastructure. This course could contribute to an MLE's knowledge of Deep Learning as it pertains to mobility and automotive engineering.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and maintain AI systems. This course could contribute to an AI Engineer's knowledge of Deep Learning, especially in the context of automotive engineering.

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Robotics Engineer

Robotics Engineers design and build robots and maintain existing implementations. This course could help build a foundation in Deep Learning that is increasingly needed for robotics projects.

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Computer Vision Engineer

Computer Vision Engineers develop machine vision systems to imitate and enhance human sight, often in real time. By teaching learners to use OpenCV for Computer Vision, this course may help build a foundation for this related field.

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Data Analyst

Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course could contribute to a Data Analyst's knowledge of Deep Learning, especially in the context of automotive engineering.

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Computer Scientist

Computer Scientists research and create new applications for computers. By teaching learners to use OpenCV for Computer Vision, this course may help build a foundation for this related field.

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Data Architect

Data Architects are responsible for designing, implementing, and maintaining an organization’s data infrastructure. These professionals are in constant need of training on the latest methodologies, and this course could help build a foundation in Deep Learning that is increasingly needed for data management.

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Computer Programmer

Computer Programmers write computer programs following the instructions provided by software developers and engineers. The programming component of this course could help one break into this highly in-demand field.

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Software Developer

Software Developers analyze user needs, create software specifications, and develop, implement, test, and maintain software programs. The programming component of this course could help one break into this highly in-demand field.

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Software Architect

Software Architects design, build, and maintain software systems. The programming component of this course could help one break into this highly in-demand field.

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Web Developer

Web Developers design and develop websites and web applications. The programming component of this course could help one break into this highly in-demand field.

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