你将学到什么
Implement Logistic Regression using Python and Numpy.
Apply Logistic Regression to solve binary classification problems.
课程概况
In this 2-hour long project-based course, you will learn how to implement Logistic Regression using Python and Numpy. Logistic Regression is an important fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of logistic regression available, it’s still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training and validation process.
Since this is a practical, project-based course, you will need to have a theoretical understanding of logistic regression, and gradient descent. We will focus on the practical aspect of implementing logistic regression with gradient descent, but not on the theoretical aspect.
By the end of this course, you would create and train a logistic model that will be able to predict if a given image is of hand-written digit zero or of hand-written digit one. The model will be able to distinguish between images or 0s and 1s, and it will do that with a very high accuracy. Not only that, your implementation of the logistic model will also be able to solve any generic binary classification problem. You will still have to train model instances on specific datasets of course, but you won’t have to change the implementation and it will be re-usable. The dataset for images of hand written digits comes from the popular MNIST dataset. This data set consists of images for the 10 hand-written digits (from 0 to 9), but since we are implementing logistic regression, and are looking to solve binary classification problems – we will work with examples of hand written zeros, and hand written ones and we will ignore examples of rest of the digits.
This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed.
Notes:
– You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
– This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
课程大纲
Deep Learning Fundamentals: Logistic Regression
Welcome to this project-based course on Logistic Regression. In this 2-hour long project-based course, you will learn how to implement Logistic Regression using Python and Numpy. Logistic Regression is an important fundamental concept in Deep Learning, and even though popular machine learning frameworks have implementations of logistic regression available, learning to implement it on your own will enable you to understand the mechanics of optimization algorithm and the training and validation process. By the end of this course, you would create and train a logistic model that will be able to predict if a given image is of hand-written digit zero or of hand-written digit one. The model will be able to distinguish between images or zeros and ones, and it will do that with a very high accuracy. Not only that, your implementation of the logistic model will also be able to solve any generic binary classification problem. You will still have to train model instances on specific datasets of course, but you won’t have to change the implementation and it will be re-usable. The dataset for images of hand written digits comes from the popular MNIST dataset. This data set consists of images for the 10 hand-written digits (from 0 to 9), but since we are implementing logistic regression, and are looking to solve binary classification problems, we will work with examples of hand written zeros and hand written ones and we will ignore examples of rest of the digits.
课程项目
Introduction
Hyperparameters
Dataset
A Mini Batch of Examples
Create Model
Forward Pass
Backward Pass
Update Parameters
Check Model Performance
Training Loop
