Low Pass Filters & High Pass Filters : Data Science Concepts

ritvikmath · Beginner ·📐 ML Fundamentals ·5y ago
What is a low pass filter? What is a high pass filter? Sobel Filter: https://en.wikipedia.org/wiki/Sobel_operator
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1 Math Team Update
Math Team Update
ritvikmath
 2 Single Variable Calculus Volume of a Sphere - Proof 1
Single Variable Calculus Volume of a Sphere - Proof 1
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 3 Single Variable Calculus Volume of a Sphere - Proof 2
Single Variable Calculus Volume of a Sphere - Proof 2
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 4 Multivariable Calculus Volume of a Sphere Proof - Triple Integrals
Multivariable Calculus Volume of a Sphere Proof - Triple Integrals
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 5 Multivariable Calculus Volume of a Sphere Proof - Double Integrals
Multivariable Calculus Volume of a Sphere Proof - Double Integrals
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 6 The Euclidian Algorithm
The Euclidian Algorithm
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 7 Proving the Chain Rule
Proving the Chain Rule
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 8 Proving the Fundamental Theorem of Calculus Part 1
Proving the Fundamental Theorem of Calculus Part 1
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 9 Proving the Fundamental Theorem of Calculus Part 2
Proving the Fundamental Theorem of Calculus Part 2
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 10 Math Puzzle - Poison Perplexity
Math Puzzle - Poison Perplexity
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 11 Math Puzzle - Poison Perplexity - Solution
Math Puzzle - Poison Perplexity - Solution
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 12 Expected Value and Variance of Continuous Random Variables (Calculus)
Expected Value and Variance of Continuous Random Variables (Calculus)
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 13 Expected Value and Variance of Discrete Random Variables (No Calculus)
Expected Value and Variance of Discrete Random Variables (No Calculus)
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 14 Array Method
Array Method
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 15 Complex Power Series and their Derivatives
Complex Power Series and their Derivatives
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 16 Distributions - Intro
Distributions - Intro
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 17 The Poisson Distribution
The Poisson Distribution
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 18 The Bernoulli Distribution
The Bernoulli Distribution
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 19 The Binomial Distribution
The Binomial Distribution
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 20 The Continuous Uniform Distribution
The Continuous Uniform Distribution
ritvikmath
 21 The Geometric Distribution
The Geometric Distribution
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 22 The Triangular Distribution
The Triangular Distribution
ritvikmath
 23 The Exponential Distribution
The Exponential Distribution
ritvikmath
 24 The Borel Distribution + Notes on Poisson Distribution
The Borel Distribution + Notes on Poisson Distribution
ritvikmath
 25 The Gamma Distribution
The Gamma Distribution
ritvikmath
 26 The Normal Distribution
The Normal Distribution
ritvikmath
 27 The Laplace Distribution
The Laplace Distribution
ritvikmath
 28 The Chi - Squared Distribution
The Chi - Squared Distribution
ritvikmath
 29 Overfitting
Overfitting
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 30 Vector Norms
Vector Norms
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 31 Truths Behind the Titanic : K-Nearest Neighbor
Truths Behind the Titanic : K-Nearest Neighbor
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 32 The Mathematics of Breakups
The Mathematics of Breakups
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 33 Sillyfish
Sillyfish
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 34 Finding Optimal Paths - Dynamic Programming
Finding Optimal Paths - Dynamic Programming
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 35 HowToDataScience : Scraping Twitter Data
HowToDataScience : Scraping Twitter Data
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 36 Decision Trees
Decision Trees
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 37 Perceptron
Perceptron
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 38 Naive Bayes
Naive Bayes
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 39 K-Nearest Neighbor
K-Nearest Neighbor
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 40 Evaluating Machine Learning Models
Evaluating Machine Learning Models
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 41 Decision Tree Pruning
Decision Tree Pruning
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 42 K-Means Clustering
K-Means Clustering
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 43 Gaussian Mixture Model
Gaussian Mixture Model
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 44 Data Science - Fuzzy Record Matching
Data Science - Fuzzy Record Matching
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 45 Time Series Talk : Autocorrelation and Partial Autocorrelation
Time Series Talk : Autocorrelation and Partial Autocorrelation
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 46 Time Series Talk : Autoregressive Model
Time Series Talk : Autoregressive Model
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 47 Time Series Talk : Moving Average Model
Time Series Talk : Moving Average Model
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 48 Time Series Talk : ARMA Model
Time Series Talk : ARMA Model
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 49 Time Series Talk : ARCH Model
Time Series Talk : ARCH Model
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 50 Time Series Talk : White Noise
Time Series Talk : White Noise
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 51 Time Series Talk : Stationarity
Time Series Talk : Stationarity
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 52 Time Series Talk : ARIMA Model
Time Series Talk : ARIMA Model
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 53 Time Series Talk : Lag Operator
Time Series Talk : Lag Operator
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 54 Time Series Talk : What is Seasonality ?
Time Series Talk : What is Seasonality ?
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 55 Time Series Talk : Seasonal ARIMA Model
Time Series Talk : Seasonal ARIMA Model
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 56 So ... What Actually is a Matrix ? : Data Science Basics
So ... What Actually is a Matrix ? : Data Science Basics
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 57 Derivative of a Matrix : Data Science Basics
Derivative of a Matrix : Data Science Basics
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 58 Basics of PCA (Principal Component Analysis) : Data Science Concepts
Basics of PCA (Principal Component Analysis) : Data Science Concepts
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 59 Eigenvalues & Eigenvectors : Data Science Basics
Eigenvalues & Eigenvectors : Data Science Basics
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 60 The Covariance Matrix : Data Science Basics
The Covariance Matrix : Data Science Basics
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⚡ AI Lesson Summary ✦ V3 skills ⚖ Mixed

This video teaches the basics of low pass filters and high pass filters in data science, including their application to signals and images, and introduces the Sobel filter for edge detection. It covers the concepts of convolution, kernel, and edge detection, and provides practical steps for applying these filters.

Key Takeaways
  1. Convolve a signal with a low-pass filter
  2. Apply a high pass filter to a signal to find areas of change
  3. Apply a low pass filter to an image to blur or reduce noise
  4. Use a second difference or second derivative as a variation of high pass filter
  5. Apply a Sobel filter to an image for horizontal and vertical edge detection
💡 The amount of blurring in a low pass filter is determined by the size of the kernel, and edges are strongest where the image is changing the most

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