python

Building Fullstack Chatbot with PyTorch, NumPy, NLTK, and Next.js – Complete Tutorial in 2024

Building Fullstack Chatbot with PyTorch, NumPy, NLTK, and Next.js – Complete Tutorial in 2024

Description:In this tutorial, we will build a fullstack chatbot using modern technologies. We will leverage PyTorch for our deep learning model, NumPy for efficient numerical computations, NLTK for natural language processing, and Next.js for the frontend. By the end of this video, you will have a comprehensive understanding of how to integrate these tools to create a powerful chatbot from scratch. https://youtu.be/NoHtQiwoanA Hashtags: Source link lol
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Getting the Actual Client IP When Using Application Load Balancer (ALB) in AWS Lambda

Getting the Actual Client IP When Using Application Load Balancer (ALB) in AWS Lambda

When I was new to AWS, I faced an interesting challenge while working on a task to digitally sign a document, which required the client's IP as part of the e-signature. Initially, I was thrilled when the implementation seemed to work perfectly the first time. However, my excitement was short-lived. During testing, I noticed that the same IP address was being returned, even when I accessed the application from different machines. It was then that I realized the IP address I was receiving was not the actual client IP but the IP of the load balancer. This discovery led me…
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Fine-tuning LLAMA 3 for Text Classification with Limited Resources

Fine-tuning LLAMA 3 for Text Classification with Limited Resources

I recently needed to classify sentences for a particular use case at work. Remembering Jeremy Howard's Lesson 4: Getting started with NLP for absolute beginners, I first adapted his notebook to fine-tune DEBERTA. It worked, but not to my satisfaction, so I was curious what would happen if I used a LLM like LLAMA 3. The problem? Limited GPU resources. I only had access to a Tesla/Nvidia T4 instance. Research led me to QLORA. This tutorial on Fine tuning LLama 3 LLM for Text Classification of Stock Sentiment using QLoRA was particularly useful. To better understand the tutorial, I adapted…
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Time Series in Data Science: Analysis of Bitcoin and Ethereum

Time Series in Data Science: Analysis of Bitcoin and Ethereum

Time series play a crucial role in Data Science, especially when analyzing financial data. The price variations of cryptocurrencies like Bitcoin and Ethereum offer an excellent opportunity to explore time series. In this article, we will analyze the price variations of Bitcoin and Ethereum in euros, using datasets ranging from 2012 to 2019 for Bitcoin and from 2015 to 2019 for Ethereum. We will also illustrate the use of some basic time series techniques with concrete examples and practical recommendations. Importing Libraries and Loading DataBefore diving into the analysis, we need to import the necessary libraries and load the datasets.…
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Build a Dynamic Photo Collection App With Django

Build a Dynamic Photo Collection App With Django

Creating a dynamic photo collection app that spans different industries is an exciting opportunity in web development. These apps, also known as lookbooks, can be used in fields like fashion, real estate, and travel to showcase visual content effectively. Recently, I developed a comprehensive lookbook using Django and Cloudinary. You can get a thorough understanding of its functionalities by watching a video walkthrough and reading a detailed description. Additionally, we'll highlight some noteworthy features that developers may find intriguing. Tip: You can fork the app from GitHub to explore its capabilities firsthand and sign up for a free Cloudinary account…
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Week 1: Exploring AI with Andrew Ng, Python Mastery, and Freelancing Adventures

Week 1: Exploring AI with Andrew Ng, Python Mastery, and Freelancing Adventures

Introduction: Hey everyone! I'm Muhammad Bilal, and this week marked the beginning of my exciting journey into the world of Python programming and Artificial Intelligence. I've started Andrew Ng's AI course and jumped into my first freelance project on Upwork. Here’s a recap of my week, filled with learning, challenges, and some exciting discoveries! This Week's Learnings: I began my week by delving deep into Python, brushing up on basic concepts like data structures and quickly advancing to more complex topics. Simultaneously, I embarked on Andrew Ng's AI course, where I learned invaluable insights about the fundamentals of Supervised Learning.…
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Use Guardrails to prevent hallucinations in generative AI applications

Use Guardrails to prevent hallucinations in generative AI applications

With Contextual grounding check, you can prevent hallucinations by detecting irrelevant and ungrounded LLM responses. Guardrails for Amazon Bedrock enables you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies. You can create multiple guardrails tailored to different use cases and apply them across multiple foundation models (FM), providing a consistent user experience and standardizing safety and privacy controls across generative AI applications. Until now, Guardrails supported four policies - denied topics, content filters, sensitive information filters, and word filters. The Contextual grounding check policy (the latest one added at the time…
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Ten Drops: A Python Pygame-CE Game Inspired by Splash Back

Ten Drops: A Python Pygame-CE Game Inspired by Splash Back

I would like to introduce my pygame project, Ten Drops - a fun and addictive water droplet game built using Pygame-CE. This game is a loving tribute to the classic Flash game "Splash Back," reimagined for modern platforms. What is Ten Drops? Ten Drops is a simple yet engaging game where players click on water droplets to make them explode, with the goal of clearing the screen. It's a perfect blend of strategy and quick reflexes that will keep you entertained for hours. Key Features Simple, intuitive gameplay Colorful water droplet graphics Inspired by the beloved Flash game "Splash Back"…
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BigQuery Schema Generation Made Easier with PyPI’s bigquery-schema-generator

BigQuery Schema Generation Made Easier with PyPI’s bigquery-schema-generator

When importing data into BigQuery, a crucial step is defining the table's structure - its schema. This schema can be auto-detected or defined manually. Auto-Detection with BigQuery’s LoadJobConfig Method (for Smaller Datasets) When we load data from a CSV file, we use the LoadJobConfig method with the autodetect parameter set to True. This tells BigQuery's data importer (bq load) to peek at the first 500 records of your data to guess its schema. This works well for smaller datasets, especially if the data originates from a well-defined source like a pre-existing database. Manual Definition: Tedious for Large & Evolving Data…
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