If you train your YOLO model only on grayscale images to detect car, then would it able to recognise a car in a colored image also. If so, then can I assume that YOLO consider only object shape not color? Kindly clarify.
I'm studying Andrew NG's Convolutional Neural Networks and am in Week 3 of the course which deals with object detection using YOLO algorithm . I don't understand one section in the programming assignment that uses a function called 'scale_boxes' . This is what is described about the function in the course materials. "*There're a few ways of representing boxes, such as via their corners or via their midpoint and height/width. YOLO converts between a few such formats at different times, …
I am following Andrew Ng's Coursera class on machine learning, and I came across this syntax for the fminunc and fmincg functions: fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)),initial_theta, options); Specifically, it is the @ that's confusing me. I read up on it and I still don't understand what we're doing with it here. Can someone clarify what this is doing, as well as how the t in the function call to lrCostFunction is related to the @(t) in the …
I am new to data science and I would like to go deep into it. I decided to start working with advanced competitions on kaggle, yet I should refine my knowledge in python and ML. I am an engineer, with 4 years of programming (basically php vanilla JS, and javascript based frameworks) I took a coirse on Udemy about ML with python. As I am already a programmer, I know most of the basics, I just need to familiarise with …
I finished a 28 hours Machine learning with python (Basic course) on Udemy, and it was very beneficial. My aim, is to be able to understand what is ML and how to use its concepts while working with data. I am confused about where to continue. My goal is to be a data engineer/analyst or at least data engineer. I am searching for courses on coursera, because of its credibility. I found a course from University of Michigan: Applied data …
Recently came across a coursera course on "How to win Kaggle competitions" where they explain how we can engineer a categorical feature from each leaf node of the decision tree. [Video Link][1] I cannot understand this concept. Any suggestion or pointers towards understanding this will be great. For example assume the following random training data: Gender Age Sample_Ftre M 25 1.5 M 26 1.5 F 28 1.5 F 27 1.5 M 26 1.5 Can anyone explain what will be the …
How and why will a linear np.random.rand() (to generate a linear scale between 0.0001 and 1) not result in better distributed result but np.power(10,-4 * np.random.rand()) (log scale) will. I am just accepting the below explanation as it is, But I can't understand the reason behind it.(simple math, i believe, which i am missing. Kindly help with some example) Reference to my query: Using an Appropriate Scale to Pick Hyperparameters To understand this, consider the number of hidden units hyperparameter. …