
Convert the image to grayscale, then reduce noise with a five by five gaussian blur to smooth the image for more accurate edge detection.
Explore neural networks inspired by biological neurons, from the perceptron to backpropagation, and learn how gradient descent trains weights to predict steering angles for autonomous driving.
Train and visualize a neural network's decision boundary with Keras predictions by plotting a 50-by-50 grid and contouring probabilities. Learn to predict a single point and evaluate model accuracy.
Leverage the Keras library to train deep neural networks, from simple perceptrons to multi-layered convolutional networks, and prepare for the next section.
Explore how neural networks combine perceptrons through weights and biases, using sigmoid activations to form nonlinear models with hidden layers for complex data classification.
Explore how backpropagation reverses feedforward to minimize cross-entropy through gradient descent, updating weights with a 0.03 learning rate in a deep network for nonlinear classification.
Explore convolutional neural networks, the go-to model for image classification, and learn how convolutional layers extract distinctive features. Discover their computational efficiency and exceptional accuracy in predictions.
Implement dropout in a convolutional neural network to reduce overfitting, evaluate with test data, and visualize convolutional features to boost MNIST accuracy.
Apply convolutional networks to classify traffic signs with strong accuracy, extending image classification skills to a traffic sign dataset, with the next section continuing this work.
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.