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AI4ALL: Image-to-Image Model
Rating: 4.2 out of 5(12 ratings)
2,053 students

AI4ALL: Image-to-Image Model

Basics and Foundation of Image-to-Image Networks
Created byYiqiao Yin
Last updated 7/2022
English

What you'll 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

Course content

2 sections • 18 lectures • 1h 45m total length

  • Introduction2:09

    This video starts with the introduction of why do we want to use autoencoder.

  • Concept of Encode-Decode4:36

    This video walks audience through the concept of encode-decode model.

  • Autoencoder8:20

    This video does a code walk through of an autoencoder.

  • Q1
  • Deep Autoencoder4:26

    This video does a code walk through of a deep autoencoder.

  • Intro to VAE1:25

    This video introduces Variational Autoencoder or VAE.

  • KL Divergence8:36

    VAE uses a special loss function that has a conventional loss penalized with KL divergence. This video walks through the concept of KL divergence.

  • VAE Code (1)6:14

    This is part 1 of the VAE code walk through.

  • Q2
  • VAE Code (2)5:10

    This is part 2 of the VAE code walk through.

  • Inference on Latent Layer3:18

    Encode-decode network models give us latent layer. Let us take advantage of this compressed representation with a lower dimension.

  • TSNE Algorithm9:43
  • TSNE Code7:26

Requirements

  • No prior programming experience needed. You will learn directly in this class.

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!



Who this course is for:

  • Pre-college level students interested in neural network models

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