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(MACH)ine learning

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241 Pins
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Carolina mach
The 3 Biggest Mistakes on Learning Data Science
Recently, I've been extensively studying machine learning and would like to share my step-by-step guide on how to build and train VGG, a classic image classification model. Meme template source: https://www.reddit.com/r/MemeTemplatesOfficial/comments/ijv8y6/this_template_was_brilliant_but_i_like_this_oc/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
The Importance of Mathematics in Machine Learning

meme

17 Pins
Introduction to Neural Machine Translation with GPUs (Part 2)
Introduction to Neural Machine Translation with GPUs (part 3)
Introduction to Neural Machine Translation with GPUs (Part 2)

neural machine translation (NMT)

4 Pins
The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - KDnuggets

neural network (deep learning)

42 Pins

support vector machine (SVM)

31 Pins

naive bayes

1 Pin
Lazy learning is a machine learning technique where the model postpones learning until a prediction is needed. It's efficient for large datasets and emphasizes instance-based learning over generalization.
30 Questions to test a data scientist on K-Nearest Neighbors (kNN)

k nearest neighbor (knn)

13 Pins

auc roc curve

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The logistic function is a sigmoid curve used in various fields, including biology, statistics, and machine learning. Represented as \( f(x) = \frac{1}{1 + e^{-x}} \), it models growth that starts exponentially but slows as it approaches a maximum limit. This function is essential in logistic regression for binary classification tasks. If you want to learn more about The logistic function, please visit our website.
Think You Don't Need Loss Functions in Deep Learning? Think Again. | Built In
Loss Functions — ML Cheatsheet documentation

loss function

3 Pins
At the very beginning of my journey to learn fundamentals of machine learning, I remember spending a lot of time in clearly understanding…
logistic regression curve
Want to learn how to build predictive models using logistic regression? This tutorial covers logistic regression in depth with theory, math, and code to help you build better models.

logistic regression

6 Pins

k mean clustering

7 Pins
r - Test significance of clusters on a PCA plot - Stack Overflow

clustering

3 Pins
Compute P Value for hierarchical clustering in R unsupervised machine learning

hierarchical cluster

1 Pin
Confusion Matrix for Machine Learning
draw the confusion matrix in the form of table python pandas. There are any references about draw the confusion matrix in the form of table python pandas in koriwright.my.id, you can look below. I hope this article about draw the confusion matrix in the form of table python pandas can be useful for you. Please remember that this article is for reference purposes only. #draw #the #confusion #matrix #in #the #form #of #table #python #pandas

confusion matrix

23 Pins
Decision tree in machine learning

decision tree

1 Pin
The journey of Gradient Descent — From Local to Global | by Pradyumna Yadav | Analytics Vidhya | Medium
Gradient descent is one of the most popular optimization algorithms used by many Machine Learning and Deep Learning algorithms. It minimizes a cost function iteratively by modifying its parameters until it finds the local minimum.

gradient descent algorithm

8 Pins
Classification algorithms are computational techniques used in machine learning and data analysis to categorize data into predefined classes based on specific features, facilitating pattern recognition and predictive modeling.

classification

2 Pins
Polynomial Regression is a special case of Linear Regression where we fit the polynomial equation on the data with a curvilinear…
Unveiling the intricacies of data analysis through a vivid scatter plot comparison of Linear vs. Polynomial Regression. #DataScience #MachineLearning #RegressionAnalysis

polynomial regression

13 Pins
Choosing the right ML model can make or break your project💡 This document lists down the pros and cons of each Machine Learning model. The models covered in this document are: ✅Linear Regression: A foundational model that predicts a continuous outcome variable based on one or more predictor variables. ✅Logistic Regression: Used for binary classification tasks. It estimates the probability that a given instance belongs to a particular category ✅Decision Tree: A flowchart-like structure wh...
Geometric Interpretation of Linear Regression - Towards Data Science
Geometric Interpretation of Linear Regression - Towards Data Science

linear regression

20 Pins