Coursera machine learning week 3 assignment

Recent Posts. I have recently completed the Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization course from Coursera by deeplearning. While doing the course we have to go through various quiz and assignments in Python. Here, I am sharing my solutions for the weekly assignments throughout the course.

These solutions are for reference only. Don't just copy-paste the code for the sake of completion. Even if you copy the code, make sure you understand the code first. Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks.

Machine learning Coursera quiz answers week 3 to week 4 - Coursera machine learning Course #Coursera

Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up your machine learning development significantly. All of these frameworks also have a lot of documentation, which you should feel free to read.

In this assignment, you will learn to do the following in TensorFlow:. Programing frameworks can not only shorten your coding time, but sometimes also perform optimizations that speed up your code. Writing and running programs in TensorFlow has the following steps:.

coursera machine learning week 3 assignment

Therefore, when we created a variable for the loss, we simply defined the loss as a function of other quantities, but did not evaluate its value. Now let us look at an easy example. Run the cell below:. Next, you'll also have to know about placeholders. A placeholder is an object whose value you can specify only later. Below, we created a placeholder for x. This allows us to pass in a number later when we run the session.

A placeholder is simply a variable that you will assign data to only later, when running the session. Here's what's happening: When you specify the operations needed for a computation, you are telling TensorFlow how to construct a computation graph. The computation graph can have some placeholders whose values you will specify only later.

Finally, when you run the session, you are telling TensorFlow to execute the computation graph. As an example, here is how you would define a constant X that has shape 3,1 :. You might find the following functions helpful:. Do not re-arrange the order. Session and run it with sess. You just implemented a linear function.

For this exercise lets compute the sigmoid function of an input. You should use the following:.Question 5 Your friend in the U. The estimated intercept is and the estimated slope is You believe that your housing market behaves very similarly, but houses are measured in square meters.

To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0. You do not need to round your answer. Note: the next quiz question will ask for the slope of the new model. Please comment below specific week's quiz blog post.

Coursera: Machine Learning (Week 3) [Assignment Solution] - Andrew NG

So that I can keep on updating that blog post with updated questions and answers. Recent Posts. The complete week-wise solutions for all the assignments and quizzes for the course " Coursera: Machine Learning by Andrew NG " is given below:.

Feel free to ask doubts in the comment section. I will try my best to answer it. If you find this helpful by any mean like, comment and share the post. This is the simplest way to encourage me to keep doing such work. Share This Facebook Twitter.

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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG

Created By ThemeXpose.This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:. Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures.

Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming e. We all know that data is important for machine learning success, but what does it really look like?

What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation. Now that you have your data sources identified, you need to bring it all together.

This week describes what you need to prepare data overall. Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects. There are so many ways data can go wrong!

coursera machine learning week 3 assignment

This week discussed some of the pitfalls in data identification and processing. Really good, The whole specialization is extremely useful for people starting in ML. Highly recommended! What is different about this course is its focus of ML applied to the real world. This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation.

Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world.

You will also be able to anticipate and mitigate common pitfalls in applied machine learning. Access to lectures and assignments depends on your type of enrollment.Hi Akshay, Please may I have theses files as well: ex2. You can get those files from Coursera assignments.

I don't have those with me now. This is the vectorized implementation of the code that's actually way more lengthier to implement using loops. In this gradient decent the number of iteration are not specified so how is the gradient decent working? So, no need of external sum function. Please try to do it on paper by yourself, you will get clear idea. Here Capital X Try to do it using pen-paper, you will get clear understanding.

Hii, thanks for your help mr. I had this one doubt about predict. Your code gave me the accuracy Can you please help me understand what's wrong with this and what's the exact difference between your code and mines'? P is a matrix with dimensions m x 1. It will work. Hi, I think you are doing this assignment in Octave and that's why you are facing this issue. Chethan Bhandarkar has provided solution for it. I have copy the exact code for plotData. Can you tel what's the problem?

I would like to know more about them. Hi there, I am trying the the same code as yours of sigmoid function but each time it is getting an error saying that 'z' undefined near line 6 column 18 error: called from sigmoid at line 6 column 5 what to do please help me out. Hello Akshay, It'd be great if you kindly share the code for "fminunc" in this week's files wherever neededcoz i don't understand that particular function well, neither did i get its solution anywhere else on internet.

Hi Ankit, Sorry but I don't have the code for "fminunc". I am facing this type of problem in matlabwhat can i do? In sigmoid error in line 6 the preallocated value assigned to variable 'g' might be unused what should i do. How's value of 'g' is unused. If you are getting some msg, it must be warning not error.Top Development Courses. Top Office Productivity Courses. Top Personal Development Courses.

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With a team of extremely dedicated and quality lecturers, machine learning week 4 assignment will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.

Clear and detailed training methods for each lesson will ensure that students can acquire and apply knowledge into practice easily. The teaching tools of machine learning week 4 assignment are guaranteed to be the most complete and intuitive.

Data for Machine Learning

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coursera machine learning week 3 assignment

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