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Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
Instructor:
Sundog Education by Frank Kane
2,908 students enrolled
English [Auto-generated]
Automatically detect lane markings in images
Detect cars and pedestrians using a trained classifier and with SVM
Classify traffic signs using Convolutional Neural Networks
Identify other vehicles in images using template matching
Build deep neural networks with Tensorflow and Keras
Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn
Process image data using OpenCV
Calibrate cameras in Python, correcting for distortion
Sharpen and blur images with convolution
Detect edges in images with Sobel, Laplace, and Canny
Transform images through translation, rotation, resizing, and perspective transform
Extract image features with HOG
Detect object corners with Harris
Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM
Classify data with artificial neural networks and deep learning

Autonomous Cars: Computer Vision and Deep Learning

The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.

As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.

The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.

Tools and algorithms we’ll cover include:

  • OpenCV

  • Deep Learning and Artificial Neural Networks

  • Convolutional Neural Networks

  • Template matching

  • HOG feature extraction

  • SIFT, SURF, FAST, and ORB

  • Tensorflow and Keras

  • Linear regression and logistic regression

  • Decision Trees

  • Support Vector Machines

  • Naive Bayes

Your instructors are Dr. Ryan Ahmed with a PhD in engineering focusing on electric vehicle control systems, and Frank Kane, who spent 9 years at Amazon specializing in machine learning. Together, Frank and Dr. Ahmed have taught over 200,000 students around the world on Udemy alone.

Students of our popular course, “Data Science, Deep Learning, and Machine Learning with Python” may find some of the topics to be a review of what was covered there, seen through the lens of self-driving cars. But, most of the course focuses on topics we’ve never covered before, specific to computer vision techniques used in autonomous vehicles. There are plenty of new, valuable skills to be learned here!

Environment Setup and Installation

1
Introduction
2
Install Anaconda, OpenCV, Tensorflow, and the Course Materials

Get everything you need for the course installed: The Anaconda scientific Python development environment, the OpenCV computer vision package, the Tensorflow package for building artificial neural networks, and the code and data that make up the course materials.

3
Test your Environment with Real-Time Edge Detection in a Jupyter Notebook

Let's try out your new environment by doing real-time edge detection on a live video stream from your webcam, within a Jupyter notebook. We'll also do a quick overview of how Jupyter notebooks work.

4
Udemy 101: Getting the Most From This Course

Introduction to Self-Driving Cars

1
A Brief History of Autonomous Vehicles

We'll cover the history of self-driving cars, which starts in 1925 and includes a lot of exciting progress that's been largely forgotten!

2
Course Overview and Learning Outcomes

We'll quickly review the course outline, and give some guidance on which sections you might be able so skip given your prior experience.

Python Crash Course [Optional]

1
Python Basics: Whitespace, Imports, and Lists

We'll briefly cover why whitespace is important in Python and how it's used, how to import packages of existing code libraries, and how to use lists in Python.

2
Python Basics: Tuples and Dictionaries

We'll continue diving into Python data structures with tuples and dictionaries, and examples of using them.

3
Python Basics: Functions and Boolean Operations

We'll cover the syntax of functions in Python, how to pass functions around as parameters, and lambda functions. We'll also see how boolean operations like equality and or work.

4
Python Basics: Looping and an Exercise

We'll see how for and while loops with in Python, and challenge you to a very simple exercise to practice what you've learned so far.

5
Introduction to Pandas

We'll walk through some examples of using the Pandas package, to slice and dice some fake video index data from cars.

6
Introduction to MatPlotLib

We'll cover the different charts that MatPlotLib can produce from our data, and how to load and view images.

7
Introduction to Seaborn

Seaborn both sits on top of Matplotlib to make it better, and introduces new kinds of visualization tools that can help you extract meaning from data. We'll walk through a bunch of examples using real fuel efficiency data for 2019 cars.

Computer Vision Basics: Part 1

1
What is computer vision and why is it important?
2
Humans vs. Computers Vision system
3
what is an image and how is it digitally stored?
4
[Activity] View colored image and convert RGB to Gray
5
[Activity] Detect lane lines in gray scale image
6
[Activity] Detect lane lines in colored image
7
What are the challenges of color selection technique?
8
Color Spaces
9
[Activity] Convert RGB to HSV color spaces and merge/split channels
10
Convolutions - Sharpening and Blurring
11
[Activity] Convolutions - Sharpening and Blurring
12
Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
13
[Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
14
[Activity] Project #1: Canny Sobel and Laplace Edge Detection using Webcam

Computer Vision Basics: Part 2

1
Image Transformation - Rotations, Translation and Resizing
2
[Activity] Code to perform rotation, translation and resizing
3
Image Transformations – Perspective transform
4
[Activity] Perform non-affine image transformation on a traffic sign image
5
Image cropping dilation and erosion
6
[Activity] Code to perform Image cropping dilation and erosion
7
Region of interest masking
8
[Activity] Code to define the region of interest
9
Hough transform theory
10
[Activity] Hough transform – practical example in python
11
Project Solution: Hough transform to detect lane lines in an image

Computer Vision Basics: Part 3

1
Image Features and their importance for object detection
2
[Activity] Find a truck in an image manually!
3
Template Matching - Find a Truck
4
[Activity] Project Solution: Find a Truck Using Template Matching
5
Corner detection – Harris
6
[Activity] Code to perform corner detection
7
Image Scaling – Pyramiding up/down
8
[Activity] Code to perform Image pyramiding
9
Histogram of colors
10
[Activity] Code to obtain color histogram
11
Histogram of Oriented Gradients (HOG)
12
[Activity] Code to perform HOG Feature extraction
13
Feature Extraction - SIFT, SURF, FAST and ORB
14
[Activity] FAST/ORB Feature Extraction in OpenCV

Machine Learning: Part 1

1
What is Machine Learning?

Let's discuss how machine learning works, and how it fits in with the world of AI and deep learning.

2
Evaluating Machine Learning Systems with Cross-Validation

Learn how train/test and K-fold cross-validation helps us to prevent "overfitting" models to the data they were trained with.

3
Linear Regression

We'll go in depth on how linear regression learns how to fit a line to observed data, to create a simple model we can use to predict new observations.

4
[Activity] Linear Regression in Action

Let's use linear regression to build a model mapping road conditions to vehicle speed.

5
Logistic Regression

Logistic regression builds a model that classifies data into one of two categories.

6
[Activity] Logistic Regression In Action

Let's practice using logistic regression to predict whether a car should go fast or slow, given distance to an upcoming bump and its size.

7
Decision Trees and Random Forests

Decision trees build up a flowchart-like model, that classifies data based on various decision points that branch off to others.

8
[Activity] Decision Trees In Action

In this activity, we'll implement the example from the previous lecture of predicting hiring decisions based on candidate attributes, and also see what happens when we use decision trees for the same logistic regression sample of predicting vehicle speed for an upcoming bump in the road.

Machine Learning: Part 2

1
Bayes Theorem and Naive Bayes

We'll cover Bayes Theorem and how it can help us understand conditional probabilities, and apply it to Naive Bayes to classify email as spam or "ham." 

2
[Activity] Naive Bayes in Action

We'll build a real spam classifier using Naive Bayes, and see how it well it works on our problem of classifying vehicle speeds based on upcoming obstacles in the road.

3
Support Vector Machines (SVM) and Support Vector Classifiers (SVC)

Support Vector Machines use the "Kernel Trick" to classify data. Hyperparameter tuning becomes important to find the right kernel to use, and the right parameters for that kernel.

4
[Activity] Support Vector Classifiers in Action

We'll apply SVC to our vehicle speed classification problem, and illustrate hyperparameter tuning to find the best kernel and best set of parameters to use.

5
Project Solution: Detecting Cars Using SVM - Part #1
6
[Activity] Detecting Cars Using SVM - Part #2
7
[Activity] Project Solution: Detecting Cars Using SVM - Part #3

Artificial Neural Networks

1
Introduction: What are Artificial Neural Networks and how do they learn?
2
Single Neuron Perceptron Model
3
Activation Functions
4
ANN Training and dataset split
5
Practical Example - Vehicle Speed Determination
6
Code to build a perceptron for binary classification
7
Backpropagation Training
8
Code to Train a perceptron for binary classification
9
Two and Multi-layer Perceptron ANN
10
Example 1 - Build Multi-layer perceptron for binary classification
11
Example 2 - Build Multi-layer perceptron for binary classification

Deep Learning and Tensorflow: Part 1

1
Intro to Deep Learning and Tensorflow

We'll talk about what Deep Learning is, and how Tensorflow works at a low level.

2
Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding.

We'll explore how to construct deep neural networks for binary and multi-class classification with Keras, the importance of normalizing your input data, and how one-hot encoding is used to translate categories into a representation that's compatible with neural nets.

3
[Activity] Building a Logistic Classifier with Deep Learning and Keras

We'll use Keras to easily experiment with a variety of network topologies to apply deep learning to our "car approaching a bump" classification problem.

4
ReLU Activation, and Preventing Overfitting with Dropout Regularlization

We'll go into more depth on activation functions and why ReLU is popular, and cover techniques for preventing overfitting including Dropout layers.

5
[Activity] Improving our Classifier with Dropout Regularization

We'll run our previous neural network longer to make some overfitting happen, and see how a Dropout layer in Keras can improve accuracy by preventing overfitting.

Deep Learning and Tensorflow: Part 2

1
Convolutional Neural Networks (CNN's)

We'll introduce the concepts of CNN's, and how they are inspired by the biology of your visual cortex.

2
Implementing CNN's in Keras

See how easy it is to implement a CNN using Tensorflow's Keras API.

3
[Activity] Classifying Images with a Simple CNN, Part 1

Let's install tensorflow-gpu to make our CNN's run faster, and prepare our image data for training our CNN.

4
[Activity] Classifying Images with a Simple CNN, Part 2

We'll classify real images from the cifar10 data set using our simple CNN, and analyze the results.

5
Max Pooling

Max Pooling reduces image data down, which can dramatically speed up the processing of your CNN.

6
[Activity] Improving our CNN's Topology and with Max Pooling

We'll improve on our image classification results by adding more layers to our CNN, and applying max pooling so we can run more epochs in a reasonable amount of time.

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