8-Chems

Tutorial: Host a Python Backend and React Frontend for Free Tutorial: Host a Python Backend and React Frontend for Free Learn how to host a Python script (backend) and a React frontend for free using popular platforms with free tiers. This tutorial, updated as of April 2025, guides you through setting up a full-stack application with a REST API backend and a static React frontend, perfect for your Google Blog project. Overview In this tutorial, we'll: Host a Python backend using PythonAnywhere or Render . Host a React frontend using Vercel or Netlify . Integrate the two using API calls with CORS handling. Provide code snippets and step-by-step instructions. React Frontend : A static site (HTML, CSS, JS) after building. Python Backend : A server running a Python script (e.g., Flask or FastAPI). Prerequisites Basic knowledge of Python, JavaScript, and React. ...

LangGraph with Groq Tutorial Using LangGraph with Groq: A Step-by-Step Tutorial This tutorial guides you through building a simple Q&A agent using LangGraph and Groq . LangGraph, part of the LangChain ecosystem, enables complex workflows with Large Language Models (LLMs). Groq provides fast, cost-effective LLM inference via its API. By the end, you'll have a working agent that answers questions using Groq's Llama 3.1 model. Prerequisites Python 3.8+ : Ensure Python is installed. Groq API Key : Sign up at console.groq.com to get your API key. Libraries : Install required packages using pip. Install Required Libraries Run the following command in your terminal to install LangChain, LangGraph, and the Groq integration: pip install langchain langchain-groq langgraph Step 1: Set Up the Groq API Key To use Groq's mod...

Docker Tutorial Docker Tutorial: Step-by-Step Guide Docker is a platform that allows you to package, distribute, and run applications in lightweight, portable containers. This tutorial provides a step-by-step guide to understanding and using Docker, culminating in a concrete example of containerizing a simple web application. Prerequisites Basic knowledge of command-line operations. A system with Docker installed (Docker Desktop for Windows/Mac or Docker Engine for Linux). Text editor (e.g., VS Code). Step 1: Install Docker Download Docker : For Windows/Mac: Install Docker Desktop . For Linux: Follow the official guide for your distribution (e.g., sudo apt-get install docker.io for Ubuntu). Verify Installation : docker --versi...

Hugging Face LLM Text Summarization Tutorial Hugging Face LLM Text Summarization Tutorial This step-by-step tutorial will guide you through creating a text summarization project using Hugging Face's Transformers library and a large language model (LLM). We'll use Python and a pre-trained model like BART to summarize text effectively. This guide is perfect for your Google Blog article! Prerequisites Basic Python knowledge Jupyter Notebook or a Python IDE Internet connection for downloading models A Hugging Face account (optional, for certain models) Step 1: Set Up Your Environment Install the required libraries in your Python environment. pip install transformers torch datasets transformers : Hugging Face's library for pre-trained models. torch : PyTorch, needed for model inference. datasets : For loading sample datasets (optional). ...

LangGraph Tutorial LangGraph Tutorial: Understanding Concepts, Functionalities, and Project Implementation Introduction LangGraph is a powerful, open-source library within the LangChain ecosystem designed to build stateful, multi-agent applications with Large Language Models (LLMs). Unlike traditional linear workflows, LangGraph enables the creation of cyclic, graph-based workflows, making it ideal for complex, agent-driven systems that require state management, conditional logic, and human-in-the-loop interactions. This tutorial explains the core concepts of LangGraph, its main functionalities, and demonstrates how to use it in a practical project—a simple customer support chatbot. Core Concepts of LangGraph 1. Graph-Based Architecture LangGraph represents workflows as directed graphs, where: Nodes : Represent individual units of work, such as an LLM call, tool invocation, or data processing function. Edge...

Building AutoML Pipelines: Cloud and Local Solutions Building AutoML Pipelines: Cloud and Local Solutions Automated Machine Learning (AutoML) streamlines model development by automating tasks like data preprocessing, feature engineering, model selection, and hyperparameter tuning. This tutorial covers two approaches: a cloud-based pipeline using Google Cloud AutoML and a local pipeline using AutoKeras. We’ll also clarify why Google Cloud AutoML cannot be run entirely locally and how AutoKeras fills that gap. Why Google Cloud AutoML Requires the Cloud Google Cloud AutoML is a powerful managed service but relies on cloud infrastructure: Architecture : It uses Google Cloud Storage for data and cloud servers for training and model management. APIs : The AutoML client communicates with Google’s servers, not local resources. Limitations : While you can preprocess data or run predictions locally, training and model ...

  Tutorial: Activation Functions in Computer Vision Tutorial: Activation Functions in Computer Vision This tutorial explores the primary activation functions used in computer vision tasks, their mathematical formulations, properties, use cases, and differences. We'll include code fragments to demonstrate their implementation and visualize their behavior using a non-linear classification problem (moons dataset). The code uses Python with TensorFlow and NumPy for implementation and Matplotlib for visualization. Table of Contents Introduction to Activation Functions Common Activation Functions ReLU (Rectified Linear Unit) Sigmoid Tanh ELU (Exponential Linear Unit) Implementation and Comparison Differences and Use Cases Concl...