#machinelearningclassification search results

๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™) Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š 1. Like & Repost 2. Comment โ€œMLโ€ 3.โ€ฆ

DAIEvolutionHub's tweet image. ๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™)

Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š

1. Like & Repost
2. Comment โ€œMLโ€
3.โ€ฆ
DAIEvolutionHub's tweet image. ๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™)

Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š

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MIT's Courses on AI & ML (FREE): โฏ 6.034 - Artificial Intelligence โฏ 6.036 - Machine Learning โฏ 6.S191 - Deep Learning โฏ 18.06 - Linear Algebra โฏ 18.S096 - Matrix Calculus โฏ 18.05 - Probability and Statistics Links inside:

swapnakpanda's tweet image. MIT's Courses on AI & ML (FREE):

  โฏ 6.034      -  Artificial Intelligence
  โฏ 6.036      -  Machine Learning
  โฏ 6.S191     -  Deep Learning
  โฏ 18.06       -  Linear Algebra
  โฏ 18.S096 -  Matrix Calculus
  โฏ 18.05       -  Probability and Statistics

Links inside:
swapnakpanda's tweet image. MIT's Courses on AI & ML (FREE):

  โฏ 6.034      -  Artificial Intelligence
  โฏ 6.036      -  Machine Learning
  โฏ 6.S191     -  Deep Learning
  โฏ 18.06       -  Linear Algebra
  โฏ 18.S096 -  Matrix Calculus
  โฏ 18.05       -  Probability and Statistics

Links inside:

Multiple-class Logistic Regression clearly explained ๐Ÿ‘‡๐Ÿป

rfeers's tweet image. Multiple-class Logistic Regression clearly explained ๐Ÿ‘‡๐Ÿป

Machine Learning: Regression Matrices-- -> Means Absolute Error -> Mean Square Error -> Root Mean Square Error -> R2 Score -> Adjusted R2 Score

mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score
mili_booo's tweet image. Machine Learning:

Regression Matrices-- 
-> Means Absolute Error 
-> Mean Square Error
-> Root Mean Square Error 
-> R2 Score
-> Adjusted R2 Score

๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™) Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š 1. Like & Repost 2. Comment โ€œMLโ€ 3.โ€ฆ

Krishnasagrawal's tweet image. ๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™)

Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š

1. Like & Repost
2. Comment โ€œMLโ€
3.โ€ฆ
Krishnasagrawal's tweet image. ๐Ÿš€ ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ก๐—ผ๐˜๐—ฒ๐˜€ (๐—›๐—ฎ๐—ป๐—ฑ๐˜„๐—ฟ๐—ถ๐˜๐˜๐—ฒ๐—ป ๐—ฃ๐——๐—™)

Master the core of ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด โ€”algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! ๐Ÿค–๐Ÿ“Š

1. Like & Repost
2. Comment โ€œMLโ€
3.โ€ฆ

If you want to stay up to date on the latest AI and Machine Learning research, reading academic papers can help. But they can be a bit intimidating and hard to understand sometimes. In this course, you'll learn how to approach and understand the theory, math, and structure inโ€ฆ

freeCodeCamp's tweet image. If you want to stay up to date on the latest AI and Machine Learning research, reading academic papers can help.

But they can be a bit intimidating and hard to understand sometimes.

In this course, you'll learn how to approach and understand the theory, math, and structure inโ€ฆ

Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing denseโ€ฆ


Level up your data science expertise! ๐Ÿ“ˆ Mastering the core mathematics is the key to truly understanding machine learning algorithms and building effective models. This visual guide breaks down the 24 most important definitions, from Gradient Descent and Linear Regression toโ€ฆ

AnalyticsVidhya's tweet image. Level up your data science expertise! ๐Ÿ“ˆ 

Mastering the core mathematics is the key to truly understanding machine learning algorithms and building effective models. This visual guide breaks down the 24 most important definitions, from Gradient Descent and Linear Regression toโ€ฆ

Six different applications of machine learning: 1. Classification 2. Regression 3. Clustering 4. Ranking 5. Recommendations 6. Anomaly Detection Here is a 4-minute video that talks about each one of these: youtube.com/watch?v=UM4a1qโ€ฆ

svpino's tweet image. Six different applications of machine learning:

1. Classification
2. Regression
3. Clustering
4. Ranking
5. Recommendations
6. Anomaly Detection

Here is a 4-minute video that talks about each one of these:
youtube.com/watch?v=UM4a1qโ€ฆ

Machine Learning: - Introduction - Types of ML: โ€ข Supervised โ€ข Unsupervised โ€ข Semisupervised โ€ข Reinforcement

mili_booo's tweet image. Machine Learning:
- Introduction
- Types of ML:

โ€ข Supervised
โ€ข Unsupervised
โ€ข Semisupervised
โ€ข Reinforcement
mili_booo's tweet image. Machine Learning:
- Introduction
- Types of ML:

โ€ข Supervised
โ€ข Unsupervised
โ€ข Semisupervised
โ€ข Reinforcement
mili_booo's tweet image. Machine Learning:
- Introduction
- Types of ML:

โ€ข Supervised
โ€ข Unsupervised
โ€ข Semisupervised
โ€ข Reinforcement
mili_booo's tweet image. Machine Learning:
- Introduction
- Types of ML:

โ€ข Supervised
โ€ข Unsupervised
โ€ข Semisupervised
โ€ข Reinforcement

Let's do a line-by-line analysis of this deep learning model and truly understand what's going on. This model identifies handwritten digits. It's one of the classic examples of machine learning applied to computer vision. ๐Ÿงต๐Ÿ‘‡

svpino's tweet image. Let's do a line-by-line analysis of this deep learning model and truly understand what's going on.

This model identifies handwritten digits. It's one of the classic examples of machine learning applied to computer vision.

๐Ÿงต๐Ÿ‘‡

Introducing DINOv3 ๐Ÿฆ•๐Ÿฆ•๐Ÿฆ• A SotA-enabling vision foundation model, trained with pure self-supervised learning (SSL) at scale. High quality dense features, combining unprecedented semantic and geometric scene understanding. Three reasons why this mattersโ€ฆ

maxseitzer's tweet image. Introducing DINOv3 ๐Ÿฆ•๐Ÿฆ•๐Ÿฆ•

A SotA-enabling vision foundation model, trained with pure self-supervised learning (SSL) at scale.
High quality dense features, combining unprecedented semantic and geometric scene understanding.

Three reasons why this mattersโ€ฆ

Logistic Regression Cheat Sheet I just turned Logistic Regression: one of the most widely used ML algorithms into a giant, beginner-friendly cheatsheet table. Instead of drowning in math, this table explains it in plain language: What it does: Predicts if something belongs toโ€ฆ

shealshakya's tweet image. Logistic Regression Cheat Sheet 

I just turned Logistic Regression: one of the most widely used ML algorithms into a giant, beginner-friendly cheatsheet table.

Instead of drowning in math, this table explains it in plain language:
What it does: Predicts if something belongs toโ€ฆ

Here is a simple machine learning model. One of the classics. If you are new, let's go together line by line and understand what's happening here: 1 of 20

svpino's tweet image. Here is a simple machine learning model. One of the classics.

If you are new, let's go together line by line and understand what's happening here:

1 of 20

Say hello to DINOv3 ๐Ÿฆ–๐Ÿฆ–๐Ÿฆ– A major release that raises the bar of self-supervised vision foundation models. With stunning high-resolution dense features, itโ€™s a game-changer for vision tasks! We scaled model size and training data, but here's what makes it special ๐Ÿ‘‡

BaldassarreFe's tweet image. Say hello to DINOv3 ๐Ÿฆ–๐Ÿฆ–๐Ÿฆ–

A major release that raises the bar of self-supervised vision foundation models.
With stunning high-resolution dense features, itโ€™s a game-changer for vision tasks!

We scaled model size and training data, but here's what makes it special ๐Ÿ‘‡
BaldassarreFe's tweet image. Say hello to DINOv3 ๐Ÿฆ–๐Ÿฆ–๐Ÿฆ–

A major release that raises the bar of self-supervised vision foundation models.
With stunning high-resolution dense features, itโ€™s a game-changer for vision tasks!

We scaled model size and training data, but here's what makes it special ๐Ÿ‘‡
BaldassarreFe's tweet image. Say hello to DINOv3 ๐Ÿฆ–๐Ÿฆ–๐Ÿฆ–

A major release that raises the bar of self-supervised vision foundation models.
With stunning high-resolution dense features, itโ€™s a game-changer for vision tasks!

We scaled model size and training data, but here's what makes it special ๐Ÿ‘‡
BaldassarreFe's tweet image. Say hello to DINOv3 ๐Ÿฆ–๐Ÿฆ–๐Ÿฆ–

A major release that raises the bar of self-supervised vision foundation models.
With stunning high-resolution dense features, itโ€™s a game-changer for vision tasks!

We scaled model size and training data, but here's what makes it special ๐Ÿ‘‡

Multiple-class Logistic Regression clearly explained ๐Ÿ‘‡๐Ÿป

rfeers's tweet image. Multiple-class Logistic Regression clearly explained ๐Ÿ‘‡๐Ÿป

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