Artificial Intelligence

If I were to start Computer Science in 2023, - Harvard - Stanford - MIT - IBM - Telegram - Microsoft - Google ❯ CS50 from Harvard http://cs50.harvard.edu/x/2023/certificate/ ❯ C/C++ http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/ ❯ Python http://cs50.harvard.edu/python/2022/ https://t.me/dsabooks ❯ SQL http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql https://t.me/sqlanalyst ❯ DSA http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/ https://t.me/crackingthecodinginterview/290 ❯ Java http://learn.microsoft.com/shows/java-for-beginners/ https://t.me/Java_Programming_Notes ❯ JavaScript http://learn.microsoft.com/training/paths/web-development-101/ https://t.me/javascript_courses ❯ TypeScript http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/ ❯ C# http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07 ❯ Mathematics (incl. Statistics) ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum ❯ Data Science cognitiveclass.ai/courses/data-science-101 https://t.me/datasciencefun/1141 ❯ Machine Learning http://developers.google.com/machine-learning/crash-course ❯ Deep Learning introtodeeplearning.com t.me/machinelearning_deeplearning/ ❯ Full Stack Web (HTML/CSS) pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05 t.me/webdevcoursefree/594 ❯ OS, Networking ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/ ❯ Compiler Design online.stanford.edu/courses/soe-ycscs1-compilers Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍

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If you're into deep learning, then you know that students usually one of the two paths: - Computer vision - Natural language processing (NLP) If you're into NLP, here are 5 fundamental concepts you should know: 👇👇 https://t.me/generativeai_gpt/7

AI/ML roadmap Topic: Mathematics - Subtopic: Linear Algebra - Vectors, Matrices, Eigenvalues and Eigenvectors - Subtopic: Calculus - Differentiation, Integration, Partial Derivatives - Subtopic: Probability and Statistics - Probability Theory, Random Variables, Statistical Inference Topic: Programming - Subtopic: Python - Python Basics, Libraries like NumPy, Pandas, Matplotlib Topic: Machine Learning - Subtopic: Supervised Learning - Linear Regression, Logistic Regression, Decision Trees - Subtopic: Unsupervised Learning - Clustering, Dimensionality Reduction[1](https://i.am.ai/roadmap) - Subtopic: Neural Networks and Deep Learning - Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks Topic: Specializations - Subtopic: Natural Language Processing - Text Preprocessing, Topic Modeling, Word Embeddings - Subtopic: Computer Vision - Image Processing, Object Detection, Image Segmentation - Subtopic: Reinforcement Learning - Markov Decision Processes, Q-Learning, Policy Gradients Join for more: https://t.me/machinelearning_deeplearning

You never got that kind of Physics in school    I'm hooked on this crazy Harvard professor's video experiments. He illustrates interesting and simple Physics   — Melted steel + liquid = ?    — What if an atomic bomb is detonated in the Mariana Trench?    — Attempts to drown an anvil in mercury    Subscribe 👉 Physics on fingers Subscribe 👉 Physics on fingers Subscribe 👉 Physics on fingers

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95% of Machine Learning solutions in the real world are for tabular data. Not LLMs, not transformers, not agents, not fancy stuff. Learning to do feature engineering and build tree-based models will open a ton of opportunities.

𝗚𝗶𝘁 𝗠𝗲𝗿𝗴𝗲 𝘃𝘀 𝗥𝗲𝗯𝗮𝘀𝗲 One of the most powerful Git features is branching. Yet, while working with it, we must integrate changes from one branch into another. The way how to do this can be different. We have two ways to do it: 𝟭. 𝗠𝗲𝗿𝗴𝗲 When you merge Branch A into Branch B (with 𝚐𝚒𝚝 𝚖𝚎𝚛𝚐𝚎), Git creates a new merge commit. This commit has two parents, one from each branch, symbolizing the confluence of histories. It's a non-destructive operation, preserving the exact history of your project, warts, and all. Merges are particularly useful in collaborative environments where maintaining the integrity and chronological order of changes is essential. Yet, merge commits can clutter the history, making it harder to follow specific lines of development. 𝟮. 𝗥𝗲𝗯𝗮𝘀𝗲 When you rebase Branch A onto Branch B (with 𝚐𝚒𝚝 𝚛𝚎𝚋𝚊𝚜𝚎), you're essentially saying, "Let's pretend these changes from Branch A were made on top of the latest changes in Branch B." Rebase rewrites the project history by creating new commits for each commit in the original branch. This results in a much cleaner, straight-line history. Yet, it could be problematic if multiple people work on the same branch, as rebasing rewrites history, which can be challenging if others have pulled or pushed the original branch. So, when to use them: 🔹 𝗨𝘀𝗲 𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘁𝗼 𝗽𝗿𝗲𝘀𝗲𝗿𝘃𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝘆, especially on shared branches or for collaborative work. It's ideal for feature branches to merge into a main or develop branch. 🔹 𝗨𝘀𝗲 𝗿𝗲𝗯𝗮𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀 or when you want a clean, linear history for easier tracking of changes. Remember to rebase locally and avoid pushing rebased branches to shared repositories. Also, be aware 𝗻𝗼𝘁 𝘁𝗼 𝗿𝗲𝗯𝗮𝘀𝗲 𝗽𝘂𝗯𝗹𝗶𝗰 𝗵𝗶𝘀𝘁𝗼𝗿𝘆. If your branch is shared with others, rebasing can rewrite history in a way that is disruptive and confusing to your collaborators.

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