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This is a course which introduces you to the most rapidly changing technologies like unlocking phones or doors by face recognition, self-driving cars which can also feature other cars and pedestrians in the street.

This course is opening brand new doors for applications which were unimaginable a few years ago. We aim to enable you to invent some new products or applications or create new algorithms after completing this course.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success

 

Course Credit: Deep Learning AI

Course Curriculum

Module 01
Neural Networks – Computer Vision 00:06:00
Neural Networks – Edge Detection Example 00:00:00
Neural Networks – More Edge Detection 00:08:00
Neural Networks – Padding 00:10:00
Neural Networks – Strided Convolutions 00:09:00
Neural Networks – Convolutions Over Volume 00:11:00
Neural Networks – One Layer of a Convolutional Network 00:16:00
Neural Networks – Simple Convolutional Network Example 00:09:00
Module 02
Neural Networks – Pooling Layers 00:10:00
Neural Networks – CNN Example 00:13:00
Neural Networks – Why Convolutions 00:10:00
Neural Networks – Why look at case studies 00:03:00
Neural Networks – Classic Networks 00:18:00
Neural Networks – ResNets 00:07:00
Neural Networks – Why ResNets Work 00:09:00
Neural Networks – Networks in Networks and 1×1 Convolutions 00:07:00
Neural Networks – Inception Network Motivation 00:10:00
Neural Networks – Inception Network 00:09:00
Neural Networks – Using Open Source Implementation 00:05:00
Module 03
Neural Networks – Transfer Learning 00:09:00
Neural Networks – Data Augmentation 00:10:00
Neural Networks – State of Computer Vision 00:13:00
Neural Networks – Object Localization 00:12:00
Neural Networks – Landmark Detection 00:06:00
Neural Networks – Object Detection 00:06:00
Neural Networks – Convolutional Implementation of Sliding Windows 00:11:00
Neural Networks – Bounding Box Predictions 00:15:00
Neural Networks – Intersection Over Union 00:04:00
Neural Networks – Non max Suppression 00:08:00
Module 04
Neural Networks – Anchor Boxes 00:10:00
Neural Networks – YOLO Algorithm 00:00:00
Neural Networks – Region Proposals 00:06:00
Neural Networks – What is face recognition 00:05:00
Neural Networks – One Shot Learning 00:05:00
Neural Networks – Siamese Network 00:05:00
Neural Networks – Triplet Loss 00:16:00
Neural Networks – Face Verification and Binary Classification 00:06:00
Neural Networks – What is neural style transfer 00:02:00
Neural Networks – What are deep ConvNets learning 00:08:00
Neural Networks – Cost Function 00:00:00
Neural Networks – Content Cost Function 00:04:00
Neural Networks – Style Cost Function 00:13:00
Neural Networks – 1D and 3D Generalizations 00:09:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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