If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/7ws1ucdG9HAvi Chawla (@_avichawla):A popular LLM interview question:"Explain the 4 stages of training LLMs from scratch."(step-by-step explanation below) https://t.co/43WiCQuJfc ...

There are primarily 4 stages of building LLMs from scratch:- Pre-training- Instruction fine-tuning- Preference fine-tuning- Reasoning fine-tuningLet's understand each of them!0️⃣ Randomly initialized LLMAt this point, the model knows nothing.You ask it “What is an LLM?” and get gibberish like “try peter hand and hello 448Sn”.It hasn’t seen any data yet and possesses just random weights.1️⃣ Pre-trainingThis stage teaches the LLM the basics of language by training it on massive corpora to predict the next tok ...

A popular LLM interview question:"Explain the 4 stages of training LLMs from scratch."(step-by-step explanation below) https://t.co/43WiCQuJfc ...

RT Avi Chawla (@_avichawla)Bagging vs Boosting in ML, explained visually: https://t.co/7YOYTJiWKa ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/2LaIt3GwjaAvi Chawla (@_avichawla):One framework to train, finetune & deploy AI models in a few lines of code!PyTorch Lightning abstracts the boilerplate code of PyTorch and provides 40+ advanced features to do professional AI research at scale by just specifying parameters.100% open-source with 30k+ stars! https://t.co/OG64ublpjQ ...

Repository Information - GitHub repository link provided for access [1] - Encouragement to star the GitHub repository [1]

One framework to train, finetune & deploy AI models in a few lines of code!PyTorch Lightning abstracts the boilerplate code of PyTorch and provides 40+ advanced features to do professional AI research at scale by just specifying parameters.100% open-source with 30k+ stars! https://t.co/OG64ublpjQ ...

Machine Learning Techniques - The article visually explains Bagging vs Boosting in Machine Learning [1] Algorithm Comparison - The content focuses on visually differentiating between Bagging and Boosting algorithms [1]

Feature Engineering Techniques - Trigonometric functions, specifically sine and cosine, are useful for encoding cyclical features due to their periodicity, boundedness, and definition for all real values [4] - Cyclical feature engineering is crucial to capture the cyclic nature of features, otherwise crucial information is lost [3] - Standard linear representation of cyclical features does not fulfill the properties of proximity between recurring values and equal distance [3] Cyclical Features - Datasets have cyclical features with a recurring pattern, exhibiting periodic behavior [1] - Examples of cyclical features include hour of the day, day of the week, and month of the year [2][6] - When representing 'hour of a day' as a cyclical feature, the central angle (2π) denotes 24 hours [4] - For 'day of the week', the central angle (2π) represents 7 days [5] Machine Learning Model Improvement - The model will find it easier to utilize the engineered cyclical features for modeling [5] - Trigonometry can help improve machine learning models by appropriately representing cyclical features [1]

RT Avi Chawla (@_avichawla)You're in an AI engineer interview at Apple.The interviewer asks:"Siri processes 25B requests/mo.How would you use this data to improve its speech recognition?"You: "Upload all voice notes from devices to iCloud and train a model"Interview over!Here's what you missed:Modern devices (like smartphones) host a ton of data that can be useful for ML models.To get some perspective, consider the number of images you have on your phone right now, the number of keystrokes you press daily, ...