Python

Flask Development Made Easy: A Comprehensive Guide to Test-Driven Development

In this comprehensive guide, I will share my extensive experience in implementing TDD in Flask development, providing you with practical tips, code examples, and best practices. The guide covers essential aspects of TDD, including setting up the development environment, configuring Flask for testing, writing various types of tests, implementing features using the TDD workflow, and integrating continuous integration and deployment.

Category: Flask Python
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Creating Our Own Chat GPT

In June, OpenAI announced that third-party applications' APIs can be passed into the GPT model, opening up a wide range of possibilities for creating specialized agents. Our team decided to write our own chat for working with GPT4 from OpenAI and other ML/LLM models with the ability to customize for the company's internal needs.

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The Ultimate Guide to Pip

Developers may quickly and easily install Python packages from the Python Package Index (PyPI) and other package indexes by using Pip. Pip successfully handles package dependencies, ensuring the proper purchase and installation of all necessary packages.

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Understanding Python Decorators and How to Use Them Effectively

Python decorators are a super handy and flexible aspect of the language, and I think that getting the hang of them is crucial for any Python programmer. Decorators let you adjust the behavior of functions or classes while they're running, all without messing with their original code.

Category: Python
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Building a serverless web application with Python and AWS Lambda

AWS Lambda is a serverless computing solution that enables you to run code without the need for server provisioning or management. It automatically scales and manages the underlying computing resources, allowing you to focus on your application code.

Category: AWS Python
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Unsupervised Sentiment Analysis using VADER and Flair

In the realm of sentiment analysis, there are two primary approaches, supervised and unsupervised learning. Supervised learning means you need a labeled dataset to train a model, while unsupervised learning does not depend on labeled data.

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Implementing Machine Learning in Web Applications with Python and TensorFlow

The Google-developed open-source software package TensorFlow is used to create and train machine learning models. Because it functions particularly well with large-scale neural networks, it has become one of the most extensively used machine learning frameworks.

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Learn how to deploy an ML model to the web

I’ve worked as a web developer for a while, and most times, I’m amazed at how web apps such as Facebook and Instagram are able to detect and recognize objects in images. I reckon a machine learning model like MobileNet makes this possible, but how do you deploy this model to the web?

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Building Machine Learning-Enabled Web Applications with Django and Scikit-Learn Introduction

Machine Learning (ML) has become an integral part of modern web applications due to its ability to improve user experiences, streamline processes, and make data-driven decisions.

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Optimizing Database Interactions in Python: SQLAlchemy Best Practices

Databases are the lifeblood of modern applications, powering everything from simple blogs to complex e-commerce platforms. Python, one of the most popular and versatile programming languages, has a rich ecosystem of tools and libraries to interact with databases efficiently and effectively.

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