Artificial Intelligence

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Complete Roadmap to learn Generative AI in 2 months 👇👇 Weeks 1-2: Foundations 1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI. 2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning. Weeks 3-4: Machine Learning Basics 1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics. 2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics. Weeks 5-6: Deep Learning 1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes. 2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data. Weeks 7-8: Generative Models 1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). 2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models. Additional Tips: - Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings. - Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others. Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible. 2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day. Best Resources to learn Generative AI 👇👇 Learn Python for Free Prompt Engineering Course Prompt Engineering Guide Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Deep Learning Nanodegree Program with Real-world Projects Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

Best Resource to Learn Artificial Intelligence (AI) For Free 👇👇 https://imp.i115008.net/qn27PL https://i.am.ai/roadmap https://bit.ly/3h97QpE https://t.me/datasciencefun/1375 http://microsoft.github.io/AI-For-Beginners https://ai.google/education/ Share with credits: https://t.me/free4unow_backup ENJOY LEARNING 👍👍

How do you start AI and ML ? Where do you go to learn these skills? What courses are the best? There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos. What’s more important than how you start is why you start. Start with why. Why do you want to learn these skills? Do you want to make money? Do you want to build things? Do you want to make a difference? Again, no right reason. All are valid in their own way. Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started. Got a why? Good. Time for some hard skills. I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course I’ve completed courses from (in order): Treehouse / youtube( free) - Introduction to Python Udacity - Deep Learning & AI Nanodegree Coursera - Deep Learning by Andrew Ng fast.ai - Part 1and Part 2 They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that. If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI. Join for more: https://t.me/machinelearning_deeplearning 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

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

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