Последние посты
Data science/ML/AI
22 сент., 08:10
Difference Between Z Test and T Test
Data science/ML/AI
21 сент., 12:04
AI For DataScience#BookData science/ML/AI
20 сент., 09:35
🤖 50 Machine Learning Project IdeasLooking to strengthen your Machine Learning portfolio? Here are 50 project ideas ranging from beginner to advanced.🟢 Beginner 1. Iris Flower Classification 2. Titanic Survival Prediction 3. House Price Prediction 4. Student Score Prediction 5. Spam Email Detection 6. Movie Recommendation System 7. Customer Churn Prediction 8. Loan Approval Prediction 9. Wine Quality Prediction 10. Diabetes Prediction 11. Heart Disease Prediction 12. Car Price Prediction 13. Salary Prediction 14. Fake News Detection 15. Handwritten Digit RecognitionData science/ML/AI
19 сент., 11:04
SQL Roadmap
Data science/ML/AI
18 сент., 12:01
🔄 Why Cross Validation Is Better Than One Train/Test SplitImagine flipping a coin 10 times. You might get 8 heads. Does that mean the coin is biased? Not necessarily.A single train/test split can also give a misleading performance estimate.Cross Validation repeats the process multiple times using different splits. Instead of trusting one lucky result... You measure average performance across several experiments.It's a much better estimate of how your model will perform on unseen data.
Data science/ML/AI
17 сент., 16:43
переслано из @chatgpt_bds
Visual explanation of how LLMs workData science/ML/AI
17 сент., 10:45
Explanatory Data Analysis#BookData science/ML/AI
16 сент., 09:05изменён
🤖 RAG: How AI Can Answer Questions Using Your Own DataLarge Language Models are powerful, but they don't automatically know everything inside your private documents, databases, or company knowledge base.That's where Retrieval-Augmented Generation (RAG) comes in. 🔹 1. User asks a question The system receives the user's query. 🔹 2. Relevant information is retrieved The query is converted into an embedding and compared against stored documents in a vector database. 🔹 3. Context is added The most relevant information is provided to the language model as context. 🔹 4. The AI generates an answer The model uses the retrieved information to produce a more relevant response.A simple RAG pipeline looks like: Documents → Chunking → Embeddings → Vector Database → Retrieval → LLM → Answer#RAG
Data science/ML/AI
15 сент., 09:25
Data science/ML/AI
15 сент., 08:21
переслано из @bigdataspecialist
📚 What I’m learning for 2027I’ve been working in software and data science for over 8 years, but lately I’d be lying if I said I wasn’t a little worried about where our jobs are heading. 😅The future feels more uncertain than ever, so I’ve been thinking seriously about what’s actually worth learning to stay relevant in 2027 and beyond.I searched around for resources I’d personally want to invest my time in, and i figured why not sharing with you guys as well. This is my shortlist 👇🧠 1. Let’s Build GPT from Scratch, Andrej Karpathy Build a GPT yourself and finally understand what’s happening behind the API. ⏱️ ~2h 🔗 https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ🔥 2. Neural Networks: Zero to Hero, Andrej Karpathy A deeper dive into neural networks, backpropagation, language models, GPT and tokenization. ⏱️ ~19h 🔗 https://karpathy.ai/zero-to-hero.htmData science/ML/AI
14 сент., 09:05
Difference Between AI Systems: A Human Analogy
Data science/ML/AI
13 сент., 10:49
✍️ SQL JOIN Explained Visually#SQL
Data science/ML/AI
12 сент., 10:45
переслано из @programming_quizz
Data science/ML/AI
12 сент., 07:03
Machine Learning VisualizedThis is an interactive curriculum with animations and exercises that show how machine learning algorithms actually work. You can watch gradient descent, decision boundaries, neural networks, clustering, and more evolve step by step. It is great for building intuition instead of treating models as black boxes.🎬 Free Interactive + Animation Course ⏰ Duration: Self-paced 🏃♂️ Self Paced 👨🏫 Created by: Daniel Sobrado / community project 🔗 Link#MachineLearning #Interactive #Visualization #Course ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉 Join @bigdataspecialist for more 👈Machine Learning VisualizedMachine Learning Visualized - Interactive AI and ML Visualizations | Machine Learning VisualizedInteractive visualizations and animations for machine learning concepts including transformers, attention mechanisms, neural networks, and more.Data science/ML/AI
11 сент., 09:34
5 LLM quantization techniques, clearly explained:1. RTN: ignores them. Rounds every weight to the nearest grid level with no calibration data. Cheapest option, weakest at low bit widths.2. GPTQ: repairs after rounding. Quantizes a layer column by column and adjusts the remaining weights to absorb the error before moving on.3. AWQ: protects before rounding. Finds the ~1% of weight channels that matter most and scales them up so they survive quantization. Everything still ends up in plain INT4.4. LLM. int8(): isolates at inference. Outlier dimensions run in FP16, the other 99.9% run in INT8, and the results are merged.5. QAT: solves it during training. The model is fine-tuned with rounding baked into every forward pass, so it adapts to the damage before quantization is actually applied.All five produce the same artifact, a model at a fraction of its trained precision. TheyData science/ML/AI
10 сент., 10:04
Chest X-Ray Classification Project#bookData science/ML/AI
9 сент., 07:44
🗂 10 Websites for Finding Real-World DatasetsFinding good datasets is often harder than building the model. These websites cover almost every domain imaginable.1. Kaggle Datasets 2. Hugging Face Datasets 3. Google Dataset Search 4. UCI Machine Learning Repository 5. OpenML 6. Our World in Data 7. World Bank Open Data 8. data.gov 9. FiveThirtyEight Data 10. AWS Registry of Open DataYou'll rarely run out of project ideas with these bookmarked.#Datasets
Data science/ML/AI
8 сент., 09:14
Local RAG on Windows#bookData science/ML/AI
7 сент., 08:45
🎲 What Makes Random Forest "Random"?A Random Forest isn't just "many decision trees." Each tree sees a different random sample of the data.Then... At every split... It only considers a random subset of features.So instead of producing 100 identical trees... You get 100 different opinions.The final prediction is the majority vote (classification) or average (regression).The randomness is exactly what makes the forest stronger.
Data science/ML/AI
5 сент., 10:35
