AI and Machine Learning

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💡 10 Key Data Structures We Use Every Day - list: keep your Twitter feeds - stack: support undo/redo of the word editor - queue: keep printer jobs, or send user actions in-game - hash table: cashing systems - Array: math operations - heap: task scheduling - tree: keep the HTML document, or for AI decision - suffix tree: for searching string in a document - graph: for tracking friendship, or path finding - r-tree: for finding the nearest neighbor - vertex buffer: for sending data to GPU for rendering

🔗 5 Techniques to Fine-Tune Large Language Models (LLMs) 🚀 With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help: 1. LoRA (Low-Rank Adaptation) - LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient. 2. LoRA-FA (LoRA with Feature Augmentation) - This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead. 3. Vera (Virtual Embedding Regularization Adaptation) - Vera helps regularize model embeddings during fine-tuning, preventing overfitting and improving generalization across different domains. 4. Delta LoRA - An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness. 5. Prefix Tuning - Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.

🔗 Master the Top 10 Machine Learning Topics

📂 Full description As generative AI models have become increasingly popular, enterprises have started to build end-to-end applications to integrate their existing workflows with generative AI. In this course, instructor Kumaran Ponnambalam shows you how to get up and running with integration, performance management, trust, and monitoring to deliver effective and trustworthy generative AI applications at scale.Explore some of the unique characteristics and use cases for generative AI-powered applications in an enterprise setting, including available options, selection criteria, and key deployment considerations for generative AI models. Kumaran covers the basics of evaluating and fine-tuning models as well as patterns and best practices for core application design. By the end of this course, youll also be equipped with new skills to manage application performance, maintain safety and trust, and navigate some of the most important ethical and legal challenges of AI.

🔅 LLM Foundations: Building Effective Applications for Enterprises 🌐 Author: Kumaran Ponnambalam 🔰 Level: Advanced ⏰ Duration: 1h 43m

🌀 Explore design considerations and best practices for building generative AI-powered applications at enterprise scale.

📗 Topics: Large Language Models, Artificial Intelligence, Enterprise Software 📤 Join Artificial intelligence for more courses

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Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace | GitVerse GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face | GitVerse Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | GitVerse | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace | GitVerse Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

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🔗 AI for Everyone: Master the Basics Unlock the essentials of Artificial Intelligence (AI) with this free IBM course. Explore applications and key concepts like machine learning, deep learning, and neural networks. 🔗 Enroll Now

🔗 AI vs. ML

AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.

🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed. AI thinks, ML learns. Simple as that!

🔗 AI vs. ML

AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.

🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed. AI thinks, ML learns. Simple as that!

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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.

📂 Full description In this course, learn how to customize open-source AI models with one of the most common open-source models, LLaMa (Large Language Model Meta AI). Instructor Denys Linkov shares a hands-on approach to working with LLaMa, explaining LLaMa architecture, prompting, deploying, and training models. He uses a series of Python notebooks to show you how to adapt LLaMa to your use cases and employ it in an enterprise or startup environment.