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Posts

  • Application and Tips for Machine Learning

    In this post, it will mentioned about the application and tips which can help to implement Machine Learning model. Also we will cover the overfitting problem and solution with example.

    Sep 11, 2020

  • Softmax Regression

    In this post, it will cover the basic concept of softmax regression, also known as multinomial classification. And it will explain what the hypothesis and cost function, and how to solve it with gradient descent as we saw previously. Also we will try to implement it with tensorflow 2.x

    Sep 10, 2020

  • Matplotlib Tutorial

    In this post, it will cover the basic usage of matplotlib (especially on pyplot) in various ways. This post is a summary of supplement lecture note in "Probability and Statistics in Data Science using Python", offered from UCSD DSE210x

    Sep 9, 2020

  • Logistic Regression/Classification

    In this post, it will cover the basic concept of Logistic Regression, which is widely used in classification tasks. And it will explain what the hypothesis and cost function, and how to solve it with gradient descent as we saw previously.

    Sep 9, 2020

  • Counting

    In this post, it will cover the definition of sets and some examples, and shows usage in Python. This post is a summary of "Probability and Statistics in Data Science using Python", offered from UCSD DSE210x

    Sep 9, 2020

  • Matrix Decomposition

    In this post, it will be explained about Matrix decomposition. And there are various method based on the constraint. We will explain four matrix decomposition method here. This post is the summary of "Mathematical principles in Machine Learning" offered from UNIST

    Sep 8, 2020

  • Multi-variable Linear Regression

    In this post, it will cover the basic concept of Multi-variable Linear Regression. Unlike Simple Linear Regression, Multi-variable Linear Regression have several dependent variables, so its hypothesis is different from we saw in previous posts.

    Sep 8, 2020

  • Cost Minimization using Gradient Descent

    In this post, it will cover cost minimization using Gradient Descent.

    Sep 7, 2020

  • Overview of Sparse Modeling

    In this post, it will be explained about what the sparse modeling is and why this algorithm is used. This post is the summary of "Mathematical principles in Machine Learning" offered from UNIST

    Sep 7, 2020

  • Simple Linear Regression with Tensorflow

    In this post, it will cover Simple linear regression with tensorflow 2.x. Hypothesis and cost fuction will be also mentioned.

    Sep 5, 2020

  • Sets

    A summary of "Probability and Statistics in Data Science using Python", offered from UCSD DSE210x

    Sep 2, 2020

  • Linear models in Deep Neural Networks

    In this post, it will learn how linear models are used in deep neural networks. This post is the summary of "Mathematical principles in Machine Learning" offered from UNIST

    Sep 2, 2020

  • Convexity of Linear Hypothesis and Margin Bound of Linear Classifiers

    In this post, it will cover the theoretical analysis on classifications with linear hyperplanes. This post is the summary of "Mathematical principles in Machine Learning" offered from UNIST

    Sep 1, 2020

  • Maximum Margin Principle and Soft Margin Hard Margin

    In this post, it will cover the concept of Margin in the linear hypothesis model, and how it is used to build the model. This post is the summary of "Mathematical principles in Machine Learning" offered from UNIST

    Aug 31, 2020

  • Perceptron and its convergence theorem

    Perceptron algorithm is used for supervised learning of binary classification. In this post, it will cover the basic concept of hyperplane and the principle of perceptron based on the hyperplane. And explains the convergence theorem of perceptron and its proof. This post is the summary of "Mathematical principles in Machine Learning"

    Aug 30, 2020