Feature Map Size
합성곱 연산 결과 특성맵의 크기를 계산하는 방법은 아래와 같다. I_h: Height of Input I_w: Width of Input K_h: Height of Kernel K_w: Width of Kernel S: Stride O_h: Height of
합성곱 연산 결과 특성맵의 크기를 계산하는 방법은 아래와 같다. I_h: Height of Input I_w: Width of Input K_h: Height of Kernel K_w: Width of Kernel S: Stride O_h: Height of
Image Classification With Deep Learning 1. The goal of the Image Classification Project Our team participated in the Kaggle Image Classification Competition. We improved the performance of Deep Learning models
Pyramid Net Performance Improvement For Image Classification
After I completed my Machine learning Course on Coursera, I recently started to learn Deep learning which is taught by Andrew Ng.If you also want to learn Deep learning, I
Nowadays I am trying to broaden my horizons. And to do so, I think I have to learn something new! As a result, I completed the ML Course with 98%,
Why do we need to know Cost function for Logistic regression? 1. Cost function for Logistic regression The cost function of the neural network will be a generalization of the
Contents in the post based on the free Coursera Machine Learning course, taught by Andrew Ng. 1. Backpropagation Algorithm We need a Backpropagation Algorithm to minimize the cost function. To
1. Regularized Linear Regression Previously, We took two learning algorithms (Gradient descent and Normal equation) for Linear regression problems. In this post, We will deal with those two algorithms and
Read MoreML(머신러닝) :: Regularized Linear Regression – Gradient descent & Normal equation
Contents in the post based on the free Coursera Machine Learning course, taught by Andrew Ng. Previously, In the case of Linear regression, we learned about Gradient descent and Normal
Read MoreML(머신러닝) :: Regularized Logistic Regression – Gradient descent & Advanced optimization
Contents in the post based on the free Coursera Machine Learning course, taught by Andrew Ng. 1. Intuition When you use Regularization, Suppose we penalize and make , really small.