#Self Supervised Learning

Contrastive learning is a type of self-supervised learning where the goal is to learn representations by comparing pairs of data. Instead of predicting missing parts of data like other self-supervised algorithms, it teaches a model to bring similar examples (called positive pairs) closer in the embedding space while spacing different ones (called negative pairs) farther apart. For instance, two different versions of the same image (rotated and cropped) should be encoded into similar vectors, while two images of different objects should be encoded into distant vectors. There is a special loss function used called the contrastive loss, which minimizes the distance between positives and maximizes it against negatives by using a parameter called the margin. This loss function is fairly simple and depends on the squared distances between points. The result of contrastive learning is a semantic space where similar concepts are related, making it very effective for downstream tasks like clustering, retrieval, or classification with minimal labeled data. Struggling with ML/AI? Accelerate Your Learning With our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: Deepia Join our AI community for more posts like this @aibutsimple 🤖

Day 72 | Resources below ⬇️ Share this with someone interested in ML! Daily update: I am working on creating a community for us! Stay tuned, more updates and more details coming soon. I am also finishing my implementation of the b-threshold. Once I finish testing the algorithm I will share the code with you all! **Resources** Supervised Learning https://www.ibm.com/topics/supervised-learning Unsupervised Learning https://cloud.google.com/discover/what-is-unsupervised-learning Reinforcement Learning https://www.synopsys.com/ai/what-is-reinforcement-learning.html Semi-Supervised Learning https://www.altexsoft.com/blog/semi-supervised-learning/ Self-supervised Learning https://neptune.ai/blog/self-supervised-learning —- ⏳ .5 H —- #math #ml #ai #machinelearning #artificialintelligence

📍Day 4: Difference between Supervised vs Unsupervised Learning cheatsheet. ⬇️ Save it for Later👇 1. Supervised and unsupervised learning are two key approaches in machine learning. 2. In supervised learning, the model is trained with labeled data where each input is paired with a corresponding output. 3. On the other hand, unsupervised learning involves training the model with unlabeled data where the task is to uncover patterns, structures or relationships within the data without predefined outputs. ✅ Type ‘supervised’ in the comment section and we will DM the PDF version for FREE ✨ ⏰ Like this post? Go to our bio click subscribe button and subscribe to our page. Join our exclusive subscribers channel ✨ Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

Unsupervised Learning vs Supervised Learning Explained in 60 Seconds!

🚀 What is Contrastive Learning? It’s a game-changing self-supervised learning method where models learn by comparing data pairs instead of predicting missing parts. 🔹 Positive pairs → similar examples brought closer 🔹 Negative pairs → different examples pushed apart 🔹 Powered by contrastive loss with a margin parameter 🔹 Builds a semantic space for clustering, retrieval & classification with minimal labels This is the secret behind modern AI breakthroughs in computer vision, NLP, and representation learning. 💡 Want to master AI faster? 👉 Follow @deeprag.AI . . . . #ContrastiveLearning #SelfSupervisedLearning #MachineLearningExplained #AIForEveryone #RepresentationLearning #DeepLearningAI #ArtificialIntelligence #MLAlgorithms #AICommunity #deepragAI #FutureOfAI . . . . “Contrastive Learning explained” “Self-supervised learning made simple” “AI representation learning” “How AI learns without labels” “Deep learning for beginners”

Self-supervised learning is a type of machine learning that sits between supervised and unsupervised learning. Like unsupervised learning, it doesn’t rely on manually labeled data, but instead, it creates its own labels from the data itself. The key idea is to design a “pretext task” where part of the data is hidden, removed, or transformed, and the model is trained to predict or reconstruct it from the remaining information. For example, in natural language processing (NLP), a model might see a sentence with missing words and learn to fill them in. Alternatively, in computer vision (CV), an image might be partially masked, and the model learns to predict the missing pixels. By solving these tasks, the model learns useful patterns and representations of the data, which can later be applied to actual downstream tasks like classification or detection. This makes self-supervised learning powerful, since it allows us to leverage the large available amounts of unlabeled data to build models that generalize well. This ability to generalize leads to applications such as transfer learning. The big difference between self-supervised and unsupervised learning is that in self-supervised learning, you use your own inputs as the supervision (labels), while unsupervised learning does not use labels at any part of the training data, just the output. C: Deepia #deeplearning #datascience #computerscience #computerengineering

DINOv2 is a self-supervised machine learning model for computer vision. It can be used for a variety of image tasks, like image classification, object detection, and video understanding without any fine tuning. To learn more check out Paper: https://arxiv.org/pdf/2304.07193.pdf Github: https://github.com/facebookresearch/dinov2 See the post from Yann on my 2023 AI Advancements post: https://www.threads.net/@rajistics/post/C1H6pe9gXLz

K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression. Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue. KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points. If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity. Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: visually explained Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn

Pre-training uses self-supervised learning across massive datasets (text, code, web, etc.) to predict the next word. Fine-tuning takes that base model and updates its weights using labeled examples for specific tasks (e.g., summarization, medical Q&A, code generation). So, to conclude it, Pre-training is reading every book in the library. Fine-tuning is taking one specific course to master just tax law. ✨💻 . 🏷️ Day 12, 50 Day Challenge, Generative Al, Artificial Intelligence, Al, Large Language Models, OpenAl, Al Evolution, Important Concepts, Series, Al Series

Contrastive Learning Explained Contrastive learning is a powerful type of self-supervised learning focused on comparing pairs of data. 🔹 Brings similar examples (positive pairs) closer together in the embedding space 🔹 Pushes different examples (negative pairs) farther apart 🔹 Uses a special contrastive loss with a margin to balance distances 🔹 Builds a semantic space where related concepts stay connected 🔹 Enables strong performance in clustering, retrieval, and classification—with minimal labels 📌 Source: Deepia 👉 Follow @theaiprime for more clear & reliable AI insights Disclaimer: This post is for informational purposes only. Credit remains with the respective creator. . . . . . #machinelearning #deeplearning #ai #computerscience #selfsupervisedlearning #contrastivelearning #datascience

What if AI could create its own labels? In self-supervised learning, models learn from raw data by solving pretext tasks (like predicting missing words or hidden parts of an image ). This powerful approach fuels modern LLMs and vision models! Credits - Deepia Follow our AI community - @aiin_nutshell #deeplearning #machinelearning #datasciences #aiexplained #techsimplified #selfsupervisedlearning

DeepSeek drama simply explained in 90 seconds 🤖 #deepseek #ai #artificialintelligence #tech #openai #microsoft #google #nvidia #technology