AI4ALL: Image-to-Image Model

Basics and Foundation of Image-to-Image Networks
New
Rating: 4.8 out of 5 (2 ratings)
498 students
1hr 45min of on-demand video
English
English [Auto]

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

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

Instructor

Data Science, Machine Learning, and Artificial Intelligence
Yiqiao Yin
  • 4.5 Instructor Rating
  • 29 Reviews
  • 5,651 Students
  • 7 Courses

I was a PhD student in Statistics at Columbia University from September of 2020 to December of 2021. I had a B.A. in Mathematics, and an M.S. in Finance from University of Rochester. I have a wide range of research interests in representation learning: Feature Learning, Deep Learning, Computer Vision (CV), and Natural Language Processing (NLP).

I am currently a Senior Data Scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. Prior, I have held professional positions such as enterprise-level Data Scientist at a EURO STOXX 50 company Bayer, quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street. I supervise a small fund specializing in algorithmic trading (since 2011, performance is here) and real estate investment. I also run my own monetarized YouTube Channel.

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