Deep Learning, Computer Vision for Self Driving Cars

Learn the basics to advance deep learning, computer vision, of autonomous driving to give vision to a self-driving car from scratch. After this course, you will know how to build a vision-based self-driving car from scratch using Python.
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About Course

This course will take you from the basics of deep learning, computer vision, of autonomous driving to give vision to a self-driving car from scratch. After this course, you will know how to build a vision-based self-driving car from scratch using Python. This is a very hands-on course where we'll build projects using OpenCV 3, Keras, TensorFlow as well as do some cool stuff with drones. By the end of this course, you should have a good understanding of what it takes to build a self-driving car. Computer Vision for Self Driving Cars is a course designed to teach you everything about computer vision and deep learning used in self-driving cars. You will learn about image segmentation, traffic light detection, lane line detection, etc using OpenCV 3.0 and Tensorflow 1.0 with some industry-ready advanced projects.


Optimization & Gradient Descent
Object Detection
3D Computer Vision
Neural Network
Forward Backward Propagation
Convolutional Net
CNN Architectures
Semantic Segmentation
Object tracking
Stereo Vision
Optical flow
Self-Driving Cars basics
Mathematics behind DL
Research papers in DL
Traffic-light detection
Lane line detection
Advance Computer Vision



Self-Driving Car basics & Intro to Deep Learning

- Intro to Deep Learning

- Human brain & perceptron

- Multi-layer perceptron or neural network

- Mathematics behind Artificial Neural Network

- Forward & Backward Propagation, Chain rule in Artificial Neural Network

- Activation Function

- Optimization & Gradient Descent

- Transfer Learning


Convolutional Neural Network

Intro to CNN

- CNN kernels, channels, feature maps, stride, padding

- Receptive Fields

- Image Classification demo

- LeNet -

Forward & Backward Propagation in LeNet

- AlexNet

- VGGNet

- Inception, Inception 2

- Batch Normalization

- ResNet


Object detection

- Transfer learning

- Rcnn

- Fast rcnn

- Faster rcnn

- Face net

- Detectron2

- Semantic segmentation

- Masked rcnn


3D Object detection

- 3D estimation

- Geometry


Lane-line detection & Road Segmentation




-3D LaneNet


Obstacle detection, Traffic Signs/Light detection, Road Markings



- RetinaNet


Distance Estimation & 3D reconstruction


-3D estimation

-Deep learning & Geometry papers

    Stereo Vision

- IDA-3D

- DispNet

- ModuleNet


Object tracking

- Kalman filter basics

- Extended Kalman Filter

- Sort - DeepSort

Object detection using Drone




Shaheen Nabi

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Shaheen Nabi is an Autonomous Vehicles engineer, Founder and CEO at Lasso Pacific. Interested in Deep reinforcement learning research. Leading the Artificial Intelligence department at Lasso Pacific.


Rajahat Nabi

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Rajahat Nabi is Co-Founder and seed investor at Lasso Pacific with his skills in computer security and business investment. He wants to explore some unique areas in the field of space science.


Izhar Ashiq

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Izhar Ashiq is a backend developer at Lasso Pacific. He made his career in backend development at Lasso Pacific. Now learning some unique things like Machine Learning.

Frequently Asked Questions

What is the refund policy?

After you enroll in this course you can explore the technology behind Self-Driving Cars in case you are not satisfied with our services you can ask for a refund in the first 15 days. After that, we don’t give refunds.

Is financial aid available?

Yes, Lasso Pacific provides financial aid to learners who cannot afford the fee. For getting successful Financial Aid you need full the conditions written here. You'll be prompted to complete an application and will be notified if you are approved. 

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings, and projects anytime and anywhere via the web or your mobile device

Do I need GPU for practicing code?

No, you don't need to buy any expensive hardware for practicing code. We will write every line of code on Google Colab.

What are pre-requisites?

- Understanding of Python programming language.
- Understanding of basic Derivation(Calculus)
If your issue with these no worries, we have free community sessions on these for free or you can ask us for removing these barriers, the rest we will teach you in the course from basics to master in the vision for Self-Driving Cars.