The Complete Self-Driving Car Course - Applied Deep Learning [Video]
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Free ChapterIntroduction
-
Installation
-
Python Crash Course
- 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
-
Computer Vision: Finding Lane Lines
-
The Perceptron
-
Keras
-
Deep Neural Networks
-
Multiclass Classification
-
MNIST Image Recognition
-
Convolutional Neural Networks
-
Classifying Road Symbols
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Polynomial Regression
-
Behavioural Cloning
About this
video
Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing 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 one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You’ll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered 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 do the following:
- Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
- Train a perceptron-based neural network to classify between binary classes
- Train convolutional neural networks to identify 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
All the code and supporting files for this course are available at https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning
- Publication date:
- April 2019
- Publisher
- Packt
- Duration
- 18 hours 0 minutes
- ISBN
- 9781838829414
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