AI and Machine Learning

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🔢 Layers of LLM Stack

🔅 Mastering Reasoning Models: Algorithms, Optimization, and Applications 📝 Learn how to build and optimize reasoning-specialized LLMs through mastery of test-time compute scaling and GRPO. 🌐 Author: Nayan Saxena 🔰 Level: Advanced ⏰ Duration: 1h 20m 📋 Topics: Large Language Models, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

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📕 Layers of AI

If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns. Let's break it down. 🔹 Reflection You give the agent an input. The agent then "reflects" on its output, and based on feedback, improves and refines. Ideal tools to use: - Base model (e.g. GPT-4o) - Fine-tuned model (to give feedback) - n8n to set up the agent. 🔹 RAG-based You give the agent a task. The agent has the ability to query an external knowledge base to retrieve specific information needed. Ideal tools to use: - Vector Database (e.g. Pinecone). - UI-based RAG (Aidbase is the #1 tool). - API-based RAG (SourceSync is a new player on the market, highly promising). 🔹 AI Workflow This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow. Ideal tools to use: - n8n to handle the workflow. - GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests). If you can master these 3 patterns well, you can solve a very broad range of different problems.

📄 ColQwen2: document search considering visual layout ColQwen2 is a modified version of the ColPali model designed to search documents by their visual features, not just by text. 🔧 How it works: • Each page is processed as an image • Qwen2-VL is used to extract not only text but also tables, charts, layout • Multivector embeddings are created • Search is based on comparing these vectors (late interaction) 📌 Why this is needed: This approach helps to find the right documents more accurately — especially if they contain complex structure, tables, or non-standard format. Suitable for: – PDF files – Scanned documents – Presentations and reports with visual elements https://huggingface.co/docs/transformers/main/en/model_doc/colqwen2

🔅 Build with AI: Advanced Production-Ready Gradio Applications 📝 Build advanced, production-ready AI apps with Gradio, LangChain, Docker, and cloud deployment. 🌐 Author: Deepak Goyal 🔰 Level: Advanced ⏰ Duration: 1h 19m 📋 Topics: Artificial Intelligence, Application Development 🔗 Join Artificial intelligence for more courses

🚀 Top AI Algorithms & Their Use-Cases A quick reference to essential AI algorithms and how they’re applied in real projects: Supervised Learning - Linear Regression: Predicting house prices based on features - Logistic Regression: Spam email classification - Decision Trees: Customer churn prediction - Random Forest: Stock price prediction - Gradient Boosting: Credit scoring for loan approval - K-Nearest Neighbors (KNN): Movie recommendation systems - Naive Bayes: Text classification (e.g., spam or not) - Support Vector Machines (SVM): Handwriting recognition in digit datasets Unsupervised Learning - K-Means Clustering: Customer segmentation for marketing - Principal Component Analysis (PCA): Image compression - Gaussian Mixture Model (GMM): Anomaly detection in network security - Association Rule Learning: Market basket analysis in retail Deep Learning & Neural Networks - Neural Networks: Facial recognition - Recurrent Neural Networks (RNN): Sentiment analysis in text - Long Short-Term Memory (LSTM): Stock market prediction - Word Embeddings: Improving search engine relevance Optimization & Other Techniques - Genetic Algorithms: Optimize supply chain logistics - Ant Colony Optimization: Solving traveling salesman problem - Reinforcement Learning: Game playing (e.g., AlphaGo) - Natural Language Processing (NLP): Chatbots for customer support Each algorithm has unique strengths that power solutions across industries from finance and marketing to security and entertainment.

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GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace

📕 RAG Pipeline vs Self RAG vs Agentic RAG

🔅 AI Model Compression Techniques: Building Cheaper, Faster, and Greener AI 📝 Learn how to make AI models faster, smaller, and more sustainable with practical techniques like pruning, quantization, and distillation. 🌐 Author: Tejas Chopra 🔰 Level: Intermediate ⏰ Duration: 1h 55m 📋 Topics: Model Compression, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

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