AI4ALL- Image-to-Image Model

AI4ALL: Image-to-Image Model, available at Free, has an average rating of 4.25, with 18 lectures, 2 quizzes, based on 8 reviews, and has 1855 subscribers.

You will learn about 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 This course is ideal for individuals who are Pre-college level students interested in neural network models It is particularly useful for Pre-college level students interested in neural network models.

Enroll now: AI4ALL: Image-to-Image Model

Summary

Title: AI4ALL: Image-to-Image Model

Price: Free

Average Rating: 4.25

Number of Lectures: 18

Number of Quizzes: 2

Number of Published Lectures: 18

Number of Published Quizzes: 2

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: Free

Quality Status: approved

Status: Live

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

Who Should Attend

Pre-college level students interested in neural network models

Target Audiences

Pre-college level students interested in neural network models

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!

Course Curriculum

Chapter 1: Image-to-Image Networks

Lecture 1: Introduction

Lecture 2: Concept of Encode-Decode

Lecture 3: Autoencoder

Lecture 4: Deep Autoencoder

Lecture 5: Intro to VAE

Lecture 6: KL Divergence

Lecture 7: VAE Code (1)

Lecture 8: VAE Code (2)

Lecture 9: Inference on Latent Layer

Lecture 10: TSNE Algorithm

Lecture 11: TSNE Code

Chapter 2: Image Segmentation

Lecture 1: Introduction

Lecture 2: Batch Normalization

Lecture 3: Theory of Conv2DTranspose

Lecture 4: U-net Model

Lecture 5: Customized Loss: Dice Coefficient and Dice Loss

Lecture 6: Create Masks from CIFAR10 Dataset

Lecture 7: U-net: Training, Evaluation, and Prediction

Instructors

Yiqiao Yin
Data Science, Machine Learning, and Artificial Intelligence

Rating Distribution

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2 stars: 0 votes

3 stars: 2 votes

4 stars: 4 votes

5 stars: 2 votes

Frequently Asked Questions

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