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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