AI and Machine Learning

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AI Developers — finally something serious. A German company 🇩🇪 (Brainlancer GmbH) is launching a curated B2B platform on April 1st, 2026. Not a freelance marketplace. Not an agency network. A verified AI builder network. Only a few spots are still open. If you can actually ship outcomes like: • RAG / Agents in production • Automations + API integrations • FastAPI tools, internal apps, backend systems → Apply now (free + anonymous). http://assesment.brainlancer.com/?src=telegram Step 1: 5 min form Step 2: 25-30 min AI interview Step 3: short call → early access 👉 Brainlancer.com (Landingpage) 👉 https://www.linkedin.com/in/soner-catakli/ (CEO)

🔅 PyTorch Essential Training: Working with Images 📝 This course provides hands-on learning for preprocessing data, training, and evaluating a pretrained model for image classification using PyTorch. 🌐 Author: Terezija Semenski 🔰 Level: Intermediate ⏰ Duration: 1h 31m 📋 Topics: PyTorch, Deep Learning 🔗 Join Artificial intelligence for more courses

✅ AI Ethics Basics You Should Know 🧠⚖️ AI Ethics focuses on ensuring that artificial intelligence systems are developed and used in a responsible, fair, and transparent manner. 🔹 1. What is AI Ethics?  AI Ethics is the study of moral principles and practices that guide the development, deployment, and use of AI technologies. 🔹 2. Why AI Ethics is Important:  • AI systems impact millions of people  • Prevents bias and discrimination  • Ensures trust and accountability  • Protects user privacy and rights  🔹 3. Key Principles of AI Ethics:  • Fairness: Avoid bias and discrimination  • Transparency: AI decisions should be explainable  • Accountability: Humans must be responsible for AI outcomes  • Privacy: Protect user data and personal information  • Safety: AI should not cause harm  🔹 4. Common Ethical Issues in AI:  • Biased algorithms  • Data privacy violations  • Surveillance misuse  • Job displacement due to automation  • Misinformation and deepfakes  🔹 5. Real World Use Cases:  • Fair hiring systems  • Ethical facial recognition  • Responsible healthcare AI  • Bias detection in financial systems  🔹 6. Examples of AI Bias:  • Gender bias in resume screening  • Racial bias in face recognition  • Language bias in NLP models  🔹 7. How to Build Ethical AI:  • Use diverse and representative datasets  • Regularly audit models for bias  • Maintain human oversight  • Clearly document AI decisions  🔹 8. AI Ethics vs AI Governance:  • AI Ethics focuses on moral values  • AI Governance focuses on rules and regulations  • Both work together for responsible AI  🔹 9. Who is Responsible for AI Ethics?  • Developers  • Companies  • Governments  • Researchers  • End users  🔹 10. Future of AI Ethics:  • Stronger regulations  • Ethical AI certifications  • More transparent AI systems  • Human centered AI development  💡 Learning AI Ethics is essential for building trustworthy and responsible AI systems. 💬 Tap ❤️ for more!

Artificial Intelligence vs Machine Learning

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🔅 Complete Guide to NLP with R 📝 Find out how to use the R programming language to implement natural language processing (NLP) algorithms. 🌐 Author: Mark Niemann-Ross 🔰 Level: Advanced ⏰ Duration: 5h 4m 📋 Topics: Natural Language Processing, R 🔗 Join Artificial intelligence for more courses

AI Developers — finally something serious. A German company 🇩🇪 (Brainlancer GmbH) is launching a curated B2B platform on April 1st, 2026. Not a freelance marketplace. Not an agency network. A verified AI builder network. Only a few spots are still open. If you can actually ship outcomes like: • RAG / Agents in production • Automations + API integrations • FastAPI tools, internal apps, backend systems → 30-sec video https://www.youtube.com/watch?v=v3lNRgAd6AE → apply now (free + anonymous). http://assesment.brainlancer.com/?src=telegram Step 1: 5 min form Step 2: 15–20 min AI interview Step 3: short call → early access 👉 Brainlancer.com (Landingpage) 👉 https://www.linkedin.com/in/soner-catakli/ (CEO)

Basic skills needed for ai engineer 1. Programming Skills (Essential) Learn Python (most widely used in AI). Basics of libraries like NumPy, Pandas (for data handling). Understanding of loops, functions, OOPs concepts. 2. Mathematics & Statistics (Basic Level) Linear Algebra (Vectors, Matrices, Dot Product). Probability & Statistics (Mean, Variance, Standard Deviation). Basic Calculus (Derivatives, Integrals – useful for ML models) 3. Machine Learning Fundamentals Understand what Supervised & Unsupervised Learning are. Learn about Regression, Classification, and Clustering. Introduction to Neural Networks and Deep Learning. 4. Data Handling & Processing How to collect, clean, and process data for AI models. Using Pandas & NumPy to manipulate datasets. 5. AI Libraries & Frameworks Learn Scikit-learn for ML models. Introduction to TensorFlow or PyTorch for Deep Learning.

🌟 olmOCR: a tool for processing PDF documents. olmOCR is a project designed to convert PDF files and document images into structured Markdown text. It can handle equations, tables, and handwritten text, preserving the correct reading order even in the most complex multi-column layouts. olmOCR is trained with heuristics to handle common parsing and metadata errors and supports SGLang and vLLM, where it can scale from one to hundreds of GPUs, making it a unique solution for large-scale tasks. The key advantage of olmOCR is its cost-effectiveness. Processing 1 million PDF pages will cost only $190 (with GPU rental), which is about 1/32 of the cost of using the GPT-4o API for the same volume. The development team created a unique method called "document anchoring" to improve the quality of the extracted text. It uses text and metadata from PDF files to improve the accuracy of processing. Image regions and text blocks are extracted, concatenated and inserted into the model prompt. When VLM requests a plain text version of the document, the "anchored" text is used along with the rasterized page image. In tests, olmOCR showed high results compared to Marker, MinerU and GOT-OCR 2.0. During testing, olmOCR was preferred in 61.3% of cases against Marker, in 58.6% against GOT-OCR and in 71.4% against MinerU. ▶️ olmOCR release: 🟢 Model olmOCR-7B-0225-preview - retrained Qwen2-VL-7B-Instruct on dataset olmOCR-mix-0225; 🟢 Dataset olmOCR-mix-0225 - over 250 thousand pages of digital books and documents from the public domain, recognized using gpt-4o-2024-08-06 and a special prompt strategy that preserves all digital content of each page. 🟢 A set of codes for inference and training. ▶️ Recommended environment for inference: 🟠 NVIDIA GPU (RTX 4090 and above) 🟠 30 GB free space on SSD \ HDD 🟠 installed package poppler-utils 🟠 sglang with flashinfer for GPU inference ▶️ Local installation and launch:

 # Install dependencies
sudo apt-get update
sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools

# Set up a conda env
conda create -n olmocr python=3.11
conda activate olmocr

git clone https://github.com/allenai/olmocr.git
cd olmocr
pip install -e .

# Convert a Single PDF
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/test.pdf

# Convert Multiple PDFs
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/*.pdf

📌 Licensing: Apache 2.0 License. 🟡 Article 🟡 Demo 🟡 Model 🟡 Arxiv 🟡 Discord Community 🖥 Github

📌 Llama3 from scratch: extended version The "Deepdive Llama3 from scratch" project is an extended fork of the guide repository for creating LLama-3 from scratch step by step. The original project has been reworked, updated, improved and optimized in order to help everyone understand and master the implementation principle and detailed rationalization process of the Llama3 model. ▶️ Changes and improvements in this fork: 🟢 The sequence of presentation of the material has been changed, the structure has been adjusted to make the learning process more transparent, helping to understand the code step by step; 🟢 Added a large number of detailed annotations to the code; 🟢 The changes in matrix dimensions at each stage of the calculation are fully annotated; 🟢 Detailed explanations of the principles have been added to fully understand the design concept of the model. 🟢 An additional chapter dedicated to KV-cache has been added, which describes in detail the basic concepts, operating principles, and application process of the attention mechanism. 📌 Licensing: MIT License. 🔜 Repository on Github

🔅 Hugging Face Transformers: Introduction to Pretrained Models 📝 Learn how to build natural language processing (NLP) applications with pretrained transformers in Hugging Face, the popular machine learning platform. 🌐 Author: Kumaran Ponnambalam 🔰 Level: Advanced ⏰ Duration: 54m 📋 Topics: Hugging Face Products, Natural Language Processing, Transformers 🔗 Join Artificial intelligence for more courses