Deep Learning for Self Driving Cars Training Course

Course Code

selfdrivingcar

Duration

21 hours (usually 3 days including breaks)

Requirements

  • Python programming experience.

Audience

  • Developers

Overview

Deep learning is a subfield of machine learning. It uses methods based on learning data representations and structures such as neural networks.

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.

This instructor-led, live training (onsite or remote) is aimed at developers who wish to build a self-driving car using deep learning techniques.

By the end of this training, participants will be able to:

  • Use computer vision techniques to identify lanes.
  • Use Keras to build and train convolutional neural networks.
  • Train a deep learning model to differentiate traffic signs.
  • Simulate a fully autonomous car.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

Setting up the Development Environment

Creating a Project

Configuring the Simulator

Preparing the Data Sets

Overview of Python Deep Learning Libraries

Applying Computer Vision Techniques to Track Lanes

Training Perceptron-Based Neural Networks to Detect Other Vehicles

Implementing Convolutional Neural Networks to Predict Steering Angle and Speed

Training a Deep Learning Model to Classify Traffic Signs

Using Polynomial Regression to Improve Predictive Accuracy

Testing the Self Driving Car

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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