DR
Diya R
Series Details
About
Hello Everyone!
Welcome to Deep Dive into Convolution Neural Networks. In this series, I will teach you how to program your own convolution neural network (CNN) through Python and I will explain how convolution neural networks work, by providing a full outline on how the layers of the model function and a introduction into backpropagation. I want everyone to think of a image recognition problem that they want to solve before joining this series, as this series will be tailored to each individual's needs, based on the project they choose. Please keep in mind that we are strictly only working with CNNs, so do not choose a project that would involve a different type of model. Finally, at the end of this series I will provide a through explanation of how to choose and prepare a project that will get you to the International Science and Engineering Fair (ISEF), as I was given the oppourtunity to compete this year! Hope to see you soon!
NOTE: we will be using Replit and GitHub through this process. I recommend having a laptop where you can save and quickly access files, if you want to be able to deploy your CNN as a website (which we may cover this this series as well).
✋ ATTENDANCE POLICY
Please do not miss more than two sessions, as you will be unable to recieve the personalized help you deserve.
Dates
June 12 - June 19
Learners
3 / 10
Total Sessions
2
About the Tutor
DR
Hi! I am a student from a small town in Michigan. I am currently taking pre calculus and I am in 9th grade. I really want to prepare myself for the SAT and want to learn Calculus / ML learning! I hope you all can help me and I hope, in return I can help you with Algebra/ Geometry and etc!
View Diya R's Profile
Upcoming Sessions
2
12
Jun
AI
Session 1
Artificial Intelligence
Today we will install the necesary packages and prepare our coding environment. We will also share what project we want to do with each other, to get a better idea of how this series will progress.
19
Jun
AI
Session 2
Artificial Intelligence
In this session we will begin to code our CNNs. We will learn about the different layers of the CNN as well.