Artificial Intelligence

Russia is currently hosting the AI Journey international conference, during which the second season of the AI4PLANET scientific and educational video podcast was released. The main topic of this season was the role of AI in the emergence of new professions and transformation of existing ones. The speakers of the podcast discussed in 10 episodes how AI is already helping experts and what are the prospects of using AI in the work of ecologists, climatologists, doctors, teachers, HR-specialists and security officers. The experts sought answers to the burning questions: ▫️ How will AI strengthen the skills of the in-demand specialist of the future? ▫️ Do scientists and researchers already need to master Data Science skills now? ▫️ AI-developer for sustainable development - a new profession or a collective image of coordinated interdisciplinary work of a large team? AI4PLANET is a technological “journey” through professions from different fields of sustainable development: from climate and ecology to psychology and professions of the future. We invite you to visit the AI Journey international conference page and listen to the AI4PLANET video podcast.

ARTIFICIAL INTELLIGENCE 🤖 🎥 Siraj Raval - YouTube channel with tutorials about AI. 🎥 Sentdex - YouTube channel with programming tutorials. ⏱ Two Minute Papers - Learn AI with 5-min videos. ✍️ Data Analytics - blog on Medium. 🎓 Google Machine Learning Course - A crash course on machine learning taught by Google engineers. 🌐 Google AI - Learn from ML experts at Google.

AI/ML Roadmap👨🏻‍💻👾🤖 - ==== Step 1: Basics ==== 📊 Learn Math (Linear Algebra, Probability). 🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised). ==== Step 2: Machine Learning ==== 🔢 Clean & Visualize Data (Pandas, Matplotlib). 🏋️‍♂️ Learn Core Algorithms (Linear Regression, Decision Trees). 📦 Use scikit-learn to implement models. ==== Step 3: Deep Learning ==== 💡 Understand Neural Networks. 🖼️ Learn TensorFlow or PyTorch. 🤖 Build small projects (Image Classifier, Chatbot). ==== Step 4: Advanced Topics ==== 🌳 Study Advanced Algorithms (Random Forest, XGBoost). 🗣️ Dive into NLP or Computer Vision. 🕹️ Explore Reinforcement Learning. ==== Step 5: Build & Share ==== 🎨 Create real-world projects. 🌍 Deploy with Flask, FastAPI, or Cloud Platforms. #ai #ml

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10 Things you need to become an AI/ML engineer: 1. Framing machine learning problems 2. Weak supervision and active learning 3. Processing, training, deploying, inference pipelines 4. Offline evaluation and testing in production 5. Performing error analysis. Where to work next 6. Distributed training. Data and model parallelism 7. Pruning, quantization, and knowledge distillation 8. Serving predictions. Online and batch inference 9. Monitoring models and data distribution shifts 10. Automatic retraining and evaluation of models

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Hard Pill To Swallow: 💊 Robots aren’t stealing your future - they’re taking the boring jobs.  Meanwhile: - Some YouTuber made six figures sharing what she loves.  - A teen's random app idea just got funded. - My friend quit banking to teach coding - he's killing it. Here’s the thing: Hard work still matters. But the rules of the game have changed.  The real money is in solving problems, spreading ideas, and building cool stuff. Call it evolution. Call it disruption. Whatever. Crying about the old world won't help you thrive in the new one. Create something.✨ #ai

AI Engineer Deep Learning: Neural networks, CNNs, RNNs, transformers. Programming: Python, TensorFlow, PyTorch, Keras. NLP: NLTK, SpaCy, Hugging Face. Computer Vision: OpenCV techniques. Reinforcement Learning: RL algorithms and applications. LLMs and Transformers: Advanced language models. LangChain and RAG: Retrieval-augmented generation techniques. Vector Databases: Managing embeddings and vectors. AI Ethics: Ethical considerations and bias in AI. R&D: Implementing AI research papers.

What kind of problems neural nets can solve? Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.

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