MAKER
It’s been a long time since I am back to the machine learning post. Let’s take a look at what I have been dealing with. Supervised learning Supervised learning is to train data using labeled data. Given an input value (X data), it learns the Label (Y data) for the input value and typically has …
Continue reading “2021.01.17(pm): Ensemble – Bagging and Boosting”
Machine learning blind spots In 2015, there was a controversy when Google AI recognized black people as gorillas. This shows the blind spot of AI. Humans can distinguish between chicken and poodle, but because the machine separates the image with an algorithm, the pictures below can be recognized as the same picture. Similar examples include …
Continue reading “2021.01.10(am): BNN(Bayesian Neural Network)”
I wrote this post with a tutorial on ending deep learning in 60 minutes via the link below.https://tutorials.pytorch.kr/beginner/deep_learning_60min_blitz.html What is PYTORCH? A Python-based scientific computation package targeted at two groups: When computing using GPU is required while replacing NumPy When you need a deep learning research platform that provides maximum flexibility and speed First, let’s …
Continue reading “2020.08.29(pm): Deep Learning with PYTORCH”
This post was written by referring to the contents of a deep learning book from the founder of Keras. : https://www.manning.com/books/deep-learning-with-python Overfitting occurs when there are too few samples to learn. This is because you cannot train a generalizable model on new data. Given a lot of data, the model can learn all possible aspects …
Today, I’m going to cover an example of MLP (Muli-Layer Perceptron). For the concept of MLP, please refer to the article I wrote on December 8, 2019. Today, I am going to focus on practice rather than concept. I want to use Kaggle data, which is famous for its dataset, but I want to use …
Continue reading “2020.08.08(pm): MLP example – Predicting house prices”
The Gaussian distribution is another name for the normal distribution. The Gaussian distribution, widely known in statistics, is a very important concept. Last time, I mentioned the concept of probability and statistics and mentioned the central limit theorem. Let’s look at the central limit theorem again. Central limit theorem The sample data sampled from a …
This is a summary of Deep Learning Chapter4 from the founder of Keras. This time, as an example of a regression problem, we will try to predict the housing price. The data we will use today is the Boston Housing Price Dataset, which estimates the median value of housing prices given data such as crime …
Continue reading “2020.04.12(pm): Regression Example: Housing price prediction”
We have a lot to do with binary classification in everyday life. For example, there are dogs and cats, 100 won coins and 500 won coins, and iPhone and Samsung Galaxy phones. This time, I’m going to classify a movie review. Binary classification is considered to be the most widely used in machine learning. Let’s …
Continue reading “2020.04.05(pm): Binary Classification – Movie Review Classification”
This time, let’s look at the concepts of probability and statistics that are the basis of machine learning algorithms. When learning a model in the area of supervised learning, the most important thing is variable selection. Numerical interpretation and verification are required to ensure good selection of this variable. So, what is needed is the …
Continue reading “2020.03.29(pm): Statistical Inference and Hypothesis Testing”
The SVM covered in this post is a supervised learning algorithm for solving classification problems. SVM extends the input data to create more complex models that are not defined as simple hyperplanes. SVM can be applied to both classification and regression. Linear models and nonlinear characteristics Because linear and hyperplanes are not flexible, linear models …
Continue reading “2020.03.21(pm): Support Vector Machine(SVM)”