StatQuest 機器學習研習讀書會 - Cupoy

12/02：A Gentle Introduction to Machine Learning

Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the mos...

12/02：Cross Validation

One of the fundamental concepts in machine learning is Cross Validation. It's how we decide which machine learning method would be best for our dataset. Chec...

12/02：The Confusion Matrix

One of the fundamental concepts in machine learning is the Confusion Matrix. Combined with Cross Validation, it's how we decide which machine learning method...

12/02：Sensitivity and Specificity

In this StatQuest we talk about Sensitivity and Specificity - to key concepts for evaluating Machine Learning methods. These make it easier to choose which m...

12/02：ROC and AUC

ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matric...

12/02： Bias and Variance

Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your ...

12/16：The Main Ideas of Least Squares and Linear Regression

Fitting a line to data is actually pretty straightforward.⭐ NOTE: When I code, I use Kite, a free AI-powered coding assistant that will help you code faster ...

12/16：Linear Regression

The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, so let's get down to it! NOTE: This S...

12/16：Using Linear Models for t-tests and ANOVA

This StatQuest shows how the methods used to determine if a linear regression is statistically significant (covered in part 1) can be applied to t-tests and ...

12/16：Design Matrices For Linear Models

In order to use general linear models (GLMs) you need to create design matrices. At first, these can seem intimidating, but this StatQuest puts together a bu...

12/30：StatQuest: Principal Component Analysis (PCA), Step-by-Step - YouTube

Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly c...

12/30：Neural Networks Pt. 3: ReLU In Action!!! - YouTube

The ReLU activation function is one of the most popular activation functions for Deep Learning and Convolutional Neural Networks. However, the function itsel...

12/30：Neural Networks Part 5: ArgMax and SoftMax - YouTube

When your Neural Network has more than one output, then it is very common to train with SoftMax and, once trained, swap SoftMax out for ArgMax. This video gi...

12/30：Regularization Part 1: Ridge (L2) Regression - YouTube

Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model to the training data. It...

12/30：Regularization Part 2: Lasso (L1) Regression - YouTube

Lasso Regression is super similar to Ridge Regression, but there is one big, huge difference between the two. In this video, I start by talking about all of ...

12/30：Ridge vs Lasso Regression, Visualized!!! - YouTube

People often ask why Lasso Regression can make parameter values equal 0, but Ridge Regression can not. This StatQuest shows you why.NOTE: This StatQuest assu...

p-values: What they are and how to interpret them - YouTube

This StatQuest is all about interpreting p-values. You've seen them online or in publications, or heard about them, whispered in dark, rave filled dance club...

Bootstrapping 拔靴法 (待刪除)

Bootstrapping 主要概念

Bootstrapping is one of the simplest, yet most powerful methods in all of statistics. It provides us an easy way to get a sense of what might happen if we co...

The Main Ideas behind Probability Distributions - YouTube

Here we demystify what a probability distribution is. It's not complicated, and we'll build on this in the coming weeks.⭐ NOTE: When I code, I use Kite, a fr...

The Normal Distribution, Clearly Explained!!! - YouTube

The normal, or Gaussian, distribution is the most common distribution in all of statistics. Here I explain the basics of how these distributions are created ...

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The normal, or Gaussian, distribution is the most common distribution in all of statistics. Here I explain the basics of how these distributions are created ...

The Central Limit Theorem, Clearly Explained!!! - YouTube

The Central Limit Theorem is a big deal, but it's easy to understand. Here I show you what it is, then I describe why this is useful and fundamental to Stati...

Population and Estimated Parameters, Clearly Explained!!! - YouTube

One of the most basic and most important thing we can do in statistics is estimate population parameters. This video explains what population parameters are ...

Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!! - YouTube

One of the most basic things we do all the time in Data Analysis (i.e. Statistics, Machine Learning or any other sort of number crunching type thing) is calc...

Hypothesis Testing and The Null Hypothesis, Clearly Explained!!! - YouTube

One of the most basic concepts in statistics is hypothesis testing and something called The Null Hypothesis. This video breaks these concepts down into easy ...

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Entropy for data science

Entropy is a fundamental concept in Data Science because it shows up all over the place - from Decision Trees, to similarity metrics, to state of the art dim...

Cross Entropy

When a Neural Network is used for classification, we usually evaluate how well it fits the data with Cross Entropy. This StatQuest gives you and overview of ...

Cross Entropy Derivatives and Backpropagation

Here is a step-by-step guide that shows you how to take the derivative of the Cross Entropy function for Neural Networks and then shows you how to use that d...

StatQuest: Decision Trees

NOTE: This video has been updated and revised. The new version can be found here: https://youtu.be/_L39rN6gz7YThis StatQuest focuses on the machine learning ...

Gradient Boost Part 1 (of 4): Regression Main Ideas

Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a seri...

Gradient Boost Part 2 (of 4): Regression Details

Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the second part in a ser...

Gradient Boost Part 3 (of 4): Classification

This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This video gives focuses on the main idea...

Gradient Boost Part 4 (of 4): Classification Details

At last, part 4 in our series of videos on Gradient Boost. This time we dive deep into the details of how it is used for classification, going through algori...

Gradient Boost Part 3 (of 4): Classification (待刪除)

This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This video gives focuses on the main idea...