Tutorials

Auto Added by WPeMatico

How to Design Python-First Interactive Dashboards with Prefab Reactive UI Components and Static HTML Export

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Technology, Tutorials

In this tutorial, we build a Prefab application that demonstrates how to create interactive dashboards entirely in Python. We use Prefab’s component-based Python interface to design a polished operations dashboard with reactive state, charts, tables, filters, forms, tabs, alerts, metrics, and client-side actions. We generate realistic pipeline monitoring data, connect it to live UI controls, […]

How to Design Python-First Interactive Dashboards with Prefab Reactive UI Components and Static HTML Export Read More »

Crawlee for Python: Build a Web Crawling Pipeline with Robots Handling, Link Graphs, and RAG Chunk Export

agentic ai, ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we build a full Crawlee-for-Python workflow that covers environment setup, local website generation, static crawling, dynamic crawling, structured extraction, and downstream data processing. We begin by configuring a compatible Crawlee runtime with pinned Pydantic support, Playwright browser installation, persistent storage directories, and Colab-safe execution handling. We then generate a realistic local demo

Crawlee for Python: Build a Web Crawling Pipeline with Robots Handling, Link Graphs, and RAG Chunk Export Read More »

✅

How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we build an end-to-end forecasting workflow with TimeCopilot. We prepare a panel dataset containing real airline passenger data and a synthetic seasonal series with injected anomalies, then evaluate a diverse collection of statistical, foundation, and optional GPU-based forecasting models. We use rolling cross-validation and multiple error metrics to identify the strongest model,

How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection Read More »

Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Technology, Tutorials

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen. We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step

Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks Read More »

⚠

NVIDIA SkillSpector Guide: Scanning AI Skills for Security Risks with Static Analysis and SARIF Reports

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we explore how NVIDIA SkillSpector helps us evaluate AI skills for security risks before they are used in real-world workflows. We build a controlled corpus containing both benign and deliberately vulnerable skills, scan them through SkillSpector’s programmatic LangGraph workflow, and organize the resulting risk scores and findings with pandas. We then visualize

NVIDIA SkillSpector Guide: Scanning AI Skills for Security Risks with Static Analysis and SARIF Reports Read More »

How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention

ai, AI (Artificial Intelligence), Artificial Intelligence, deep learning, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we implement xFormers: a practical toolkit for building fast, memory-efficient Transformer models on GPUs. We begin by validating memory-efficient attention against a standard attention implementation, then compare their speed and memory consumption across different sequence lengths. We then examine causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi positional biases. Finally,

How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention Read More »

How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence

ai, AI (Artificial Intelligence), AI Infrastructure, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we build a workflow for using Docling Parse to analyze PDF documents at a detailed structural level. We start by preparing a stable Python environment, handling common Colab dependency issues, and generating a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then use Docling Parse

How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence Read More »

A Coding Hands-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Large-Scale Web Corpus Analytics

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Dataset, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore the FineWeb dataset through an advanced hands-on workflow. We stream a manageable sample of the dataset without downloading the full multi-terabyte corpus, inspect its schema and metadata, and analyze key fields such as URL, language, language score, and token count. We also reproduce simplified versions of FineWeb’s quality-filtering pipeline, apply

A Coding Hands-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Large-Scale Web Corpus Analytics Read More »

How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the

How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing Read More »

✅

A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Dataset, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the

A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric Read More »