AI4ALL: Image-to-Image Model


Basics and Foundation of Image-to-Image Networks

What you will learn

Learn about the basics of Image-to-Image Network models without any prior knowledge

Learn to use python to design an Image-to-Image Network model without any prior knowledge

Learn from top tier Data Scientists to build Image-to-Image Network models for production

Learn to develop your own customized Image-to-Image Network models

Description

This course is created to follow up with the AI4ALL initiatives. The course presents coding materials at a pre-college level and introduces a fundamental pipeline for a neural network model. The course is designed for the first-time learners and the audience who only want to get a taste of a machine learning project but still uncertain whether this is the career path. We will not bored you with the unnecessary component and we will directly take you through a list of topics that are fundamental for industry practitioners and researchers to design their customized neural network model.  The course focuses on the Image-to-Image Network models and introduce the important building block using Tensorflow. Important topics include Autoencoders, Variational Autoencoders, and U-net models.

This instructor team is lead by Ivy League graduate students and we have had 3+ years coaching high school students. We have seen all the ups and downs. Moreover, we want to share these roadblocks with you. This course is designed for beginner students at pre-college level who just want to have a quick taste of what AI is about and efficiently build a quick Github package to showcase some technical skills. We have other longer courses for more advanced students. However, we welcome anybody to take this course!


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English
language

Content

Introduction

Introduction
Concept of Encode-Decode
Autoencoder
Q1
Deep Autoencoder
Intro to VAE
KL Divergence
VAE Code (1)
Q2
VAE Code (2)
Inference on Latent Layer
TSNE Algorithm
TSNE Code

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