Computer Science Forum

In this tutorial, you will see how to summarize YouTube video transcriptions using [Distil Whisper large V3](https://huggingface.co/distil-whisper/distil-large-v3) and [Mistral-7b-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). Both Distill Whisper Large V3 and Mistral-7B-Instruct models are open-source and free-to-use models. The Distil Whisper large V3 model is a faster and smaller variant of the [Whisper large V3 model](https://huggingface.co/openai/whisper-large-v3), …

In my [previous articles](https://www.daniweb.com/programming/computer-science/tutorials/541732/paris-olympics-ticket-information-chatbot-with-memory-using-langchain), I explained how to develop customized chatbots using Retrieval Augmented Generation (RAG) approach in [LangChain](https://www.langchain.com/). However, I used proprietary models such as OpenAI, which can be expensive when you try to scale. In this article, I will show you how to use the open-source and free-of-cost …

In previous articles, I explained how to use natural language to interact with [PDF documents](https://www.daniweb.com/programming/computer-science/tutorials/541732/paris-olympics-ticket-information-chatbot-with-memory-using-langchain) and [SQL databases](https://www.daniweb.com/programming/computer-science/tutorials/541771/using-natural-language-to-query-sql-databases-with-python-langchain-module), using the Python [LangChain module](https://python.langchain.com/docs/get_started/introduction) and [OpenAI API](https://openai.com/blog/openai-api). In this article, you will learn how to use LangChain and OpenAI API to create a question-answering application that allows you to retrieve information …

In my previous article, I explained how I developed a simple chatbot using LangChain and Chat-GPT that can answer queries related to Paris Olympics ticket prices. However, one major drawback with that chatbot is that it can only generate a single response based on user queries. It can not answer …

I was searching for Paris Olympics ticket prices for tennis games recently. The official website directs you to a [PDF document](https://tickets.paris2024.org/obj/media/FR-Paris2024/ticket-prices.pdf) containing ticket prices and venues for all the games. However, I found the PDF document to be very hard to navigate. To make things easier, I developed a chatbot …

On March 4, 2024, [Anthropic](https://www.anthropic.com/) launched the [Claude 3 family of large language models](https://www.anthropic.com/news/claude-3-family). Anthropic claimed that its Claude 3 Opus model outperforms GPT-4 on various benchmarks. Intrigued by Anthropic's claim, I performed a simple test to compare the performances of Claude 3 Opus, [Google Gemini Pro](https://deepmind.google/technologies/gemini/#introduction), and [OpenAI's GPT-4](https://openai.com/research/gpt-4) …

In the rapidly evolving field of Natural Language Processing (NLP), open-source large language models (LLMs) are becoming increasingly popular as they are free to use. Among these, the [Mistral](https://docs.mistral.ai/models/) family of models stands out as a state-of-the-art model that is freely accessible to the public. Comparable in performance to the …

In a previous article, I explained [how to fine-tune Google's Gemma model for text classification](https://www.daniweb.com/programming/computer-science/tutorials/541544/fine-tuning-google-gemma-model-for-text-classification-in-python). In this article, I will explain how you can improve performance of a pretrained large language model (LLM) using retrieval augmented generation (RAG) technique. So, let's begin without ado. ## What is Retrieval Augmented Generation …

I am a first-year university student from China. My major is Computer Science and Technology. I have been self-learning C++and data structures and algorithms recently. May I ask how I can learn them well? Is anyone interested in being my teacher or learning with friends? (Machine translation, my English is …

On February 21, 2024, Google released [Gemma](https://ai.google.dev/gemma), a family of state-of-the-art open-source large language models (LLMs). As per initial results, its 7b (seven billion parameter) version is known to perform better than Meta's [Llama 2](https://llama.meta.com/), the previous state-of-the-art open-source LLM. As always, my first test with any new open-source LLM …

Integrating language models like ChatGPT into third-party applications has become increasingly popular due to their ability to comprehend and generate human-like text. However, it's crucial to acknowledge the limitations of ChatGPT, such as its knowledge cut-off date in September 2021 and its inability to access external sources like Wikipedia or …

In my previous article, I explained [how to convert PDF image to CSV using Multimodal Google Gemini Pro](https://www.daniweb.com/programming/computer-science/tutorials/541365/converting-pdf-image-to-csv-using-multimodal-google-gemini-pro). To do so, I wrote a Python script that passes text command to [Google Gemino Pro](https://blog.google/technology/ai/google-gemini-ai/) for extracting tables from PDF images and storing them in a CSV file. In this article, …

In this article, you will learn how to track faces within a video using the Python DeepFace library. Additionally, you'll discover how to include portions of the video background in face tracking by implementing custom methods that utilize the DeepFace library's `extract_faces()` method for face extraction. I explained how to …

In this article, you will learn to use [Google Gemini Pro](https://blog.google/technology/ai/google-gemini-ai/), a state-of-the-art multimodal generative model, to extract information from PDF and convert it to CSV files. You will use a simple text prompt to tell Google Gemini Pro about the information you want to extract. This is a valuable …

I recently tackled a challenging research task involving multimodal data for a classification problem using [TensorFlow Keras](https://www.tensorflow.org/guide/keras). One of the trickiest aspects was figuring out how to load multimodal data in batches from storage efficiently. While TensorFlow Keras offers helpful functions for batch-loading images from various sources, the documentation and …

In this article, we will compare two state-of-the-art large language models for zero-shot text classification: [Google Gemini Pro](https://deepmind.google/technologies/gemini/#introduction) and [OpenAI GPT-4](https://openai.com/research/gpt-4). Zero-shot text classification is a task where a model is trained on a set of labeled examples but can then classify new examples from previously unseen classes. This is …

## Introduction ## This tutorial explains how to perform multiple-label text classification using the [Hugging Face](https://huggingface.co/) transformers library. Hugging Face library implements advanced transformer architectures, proven to be state-of-the-art for various natural language processing tasks, including text classification. Hugging Face library provides trainable transformer models in three flavors: 1. Via …

Sentiment analysis, a subfield of Natural Language Processing (NLP), aims to discern and classify the underlying sentiment or emotion expressed in textual data. Whether it is understanding customers' opinions about a product, analyzing social media posts, or gauging public sentiment towards a political event, sentiment analysis plays a vital role …

In a [previous tutorial](https://www.daniweb.com/programming/computer-science/tutorials/541123/stock-price-prediction-using-1d-cnn-in-tensorflow-keras), I covered how to predict future stock prices using a deep learning model with 1D CNN layers. This method is effective for basic time series forecasting. Recently, I've enhanced this model by not just considering past closing prices but also factors like Open, High, Low, Volume, …

A video is a series of images, or frames, shown in rapid succession. Its frame rate, measured in frames per second (FPS), dictates the display speed. For instance, a 30 FPS video shows 30 frames each second. The frame count and frame rate determine a video's detail, smoothness, file size, …

## Introduction ## Loss functions are the driving force behind all machine learning algorithms. They quantify how well our models are performing by calculating the difference between the predicted and actual outcomes. The goal of every machine learning algorithm is to minimize this loss function, thereby improving the model’s accuracy. …

As a researcher, I have often found myself buried under a mountain of research articles, each promising insights and breakthroughs crucial for my work. The sheer volume of information is overwhelming, and the time it takes to extract the relevant data can be daunting. However, extracting meaningful information from research …

Facial emotion detection, as the name suggests, involves detecting emotions from faces in images or videos. Recently, I was working on a facial emotion detection task and came across the DeepFace library that implements various state-of-the-art facial emotion detection models. However, in my experience, the performance of the DeepFace library …

Stock price prediction is a challenging task that requires analyzing historical trends, market sentiments, economic indicators, and company performance. One of the popular methods for stock price prediction is using deep learning models, such as convolutional neural networks (CNNs). CNNs are a type of neural network that can extract features …

Chatbots are software applications that can interact with humans using natural language. They can be used for various purposes, such as customer service, entertainment, education, and more. Chatbots can be built using different techniques like rule-based systems, machine learning, or deep learning. In this article, I will focus on the …

Language modeling is the cornerstone of advanced natural language processing, forming the backbone for cutting-edge technologies like ChatGPT. At its core, it involves predicting words based on context, a fundamental principle underlying modern large language Models (LLMs). There are various techniques for language modeling, with attention mechanisms emerging as the …

In this tutorial, you will learn to fine-tune a [Hugging Face Transformers model](https://huggingface.co/docs/transformers/index) for video classification in PyTorch. The Hugging Face documentation provides an example of performing video classification using the Hugging Face Trainer with one of Hugging Face's built-in datasets. However, the process of fine-tuning a video transformer on …

## Introduction ## In the realm of computer vision, [Vision Transformers (ViTs)](https://arxiv.org/abs/2010.11929) revolutionized image processing by employing self-attention mechanisms, allowing for a non-sequential analysis of images. ViTs are instrumental in capturing intricate patterns and long-range dependencies, making them invaluable for tasks like image recognition and object detection. Hugging Face, a …

In a previous article, I showed you [how to analyze sentiments using Chat-GPT and data augmentation techniques](https://www.daniweb.com/programming/computer-science/tutorials/540502/sentiment-analysis-with-data-augmentation-using-chatgpt#post2293643). Following that, some readers reached out, asking for a breakdown of fine-tuning a Chat-GPT model. In this article, I will guide you through fine-tuning your Chat-GPT model using your own data. First, I'll …

In one of my research projects, I needed to extract text from video files and create a CSV file that included sentiments expressed in the text. Manual extraction was time-consuming and costly. So, I explored Automatic Speech Recognition (ASR) systems and discovered OpenAI [Whisper](https://openai.com/research/whisper), known for its high accuracy in …

In my recent journey of developing various AI solutions powered by Language Models (LLMs), a significant question has emerged: Should we harness the capabilities of Retrieval Augmented Generation (RAG), or should we opt for the path of custom fine-tuning? This decision can profoundly impact the performance and adaptability of our …

Construct an algorithm that has input an integer n ≥ 1, numbers x0, x1,...,xn, and a number x and that produces as output the product (x-xo)(x-x1)(x - xn).

Data annotation for text classification is time-consuming and expensive. In the case of smaller training datasets, pre-trained ChatGPT models might achieve higher classification accuracy on test sets than training classifiers from scratch or fine-tuning existing models. Additionally, ChatGPT can aid in annotating data for fine-tuning text classification models. In this …

I am trying to understand Boyer Moore algorithm & KMP algorithm (Knuth Morris Pratt)? I tried some places like GeeksForGeeks, TutorialsPoint etc. But I have still some doubts. If you guys have some resources or videos where these algorithms are explained in somewhat simple terms, please share them. First I …

A small airline has just purchased a computer for its new automated reservations system. You have been asked to develop the new system. You’re to write an application to assign seats on each flight of the airline’s only plane (the capacity of the plane is 10 seats of the same …

A/B testing is a method of comparing two versions of a web page or app to determine which one performs better. It is a powerful tool for optimizing websites and apps, as it allows businesses to make data-driven decisions about the design and functionality of their online platforms. With A/B …

will you kindly tell me how to make Instagram bots, Instagram bot followers? What is the step-by-step approach of it with programming? I want to make it by myself.

## Introduction ## In this tutorial, you will see how to convert the text in CSV file columns to other languages using the [DeepL API](https://www.deepl.com/translator) in the Python programing language. DeepL is one of the most popular and accurate text translation platforms. DeepL, as the name suggests, incorporates advanced deep …

## Introduction ## I was working on a problem where I had to scrape tweets related to the T20 Cricket World Cup 2022, which is currently taking place in Australia. I wanted tweets containing location names (cities) and the keyword “T20”. In the response, I want the user names of …

**Numerologists claim to be able to determine a person's character traits based on the "numeric value" of a name. The value of a name is determined by summing up the values of the letters of the name. For example, the name *Zelle* would have the value 26 + 5 + …

## Introduction ## I was recently working on a project that required me to extract location information from the [OpenStreetMap](https://www.openstreetmap.org/#map=15/51.5226/-0.1567), an open license map database of the world. The OpenStreetMap database allows you to extract location data along with the location meta information in the form of tags. My task …

hello, I am currently struggling so bad in understanding Java and how to implement the codes. I have to implement two subclasses for Canadian and US postal code in Java. For Canadian Postal code, a valid postal postal code has the rule: positions at 0,2,5 are letters. Positions at 1,4,6 …

In my [previous articles](https://www.daniweb.com/programming/computer-science/tutorials/538512/finding-inter-annotator-agreement-between-three-annotators-in-python#post2287428), I explained how you could apply heuristic and statistical approaches for finding inter-annotator agreement between multiple annotators. However, while applying those approaches, I found that finding inter-annotator agreement in the case of multi-label ranked data is a difficult task, and traditional inter-annotator agreement techniques will almost …

Can someone please give me tips on how to accomplish generating a random number within a random range. See 'roll_dice' #include <iostream> #include <limits> #include <ctime> #include <iomanip> #include <cstdlib> using namespace std; using std::cout; using std::cin; struct Sanity { unsigned int turns = std::numeric_limits<int>::max(); unsigned int sanity = std::numeric_limits<int>::max(); …

Is there a way to know if the lists inside a list contain the same elements with python? For example: Return True if given list [['A', 'B'], ['A', 'B'], ['A', 'B']] or False if given list [['B', 'C'], ['Z', 'C']]

In my [previous tutorial](https://www.daniweb.com/programming/computer-science/tutorials/538512/finding-inter-annotator-agreement-between-three-annotators-in-python), I explained how I implemented heuristic approaches for finding inter-annotator agreement between three annotators. Heuristic approaches are excellent for understanding the degree of agreement between multiple annotators. However, you should back your analysis with statistical evidence. This is where statistical techniques for inter-annotator agreement come into …

Create a java application that will convert unit length from Metric to English system. The user will be asked to enter a Metric unit and then it will display the equivalent value to unit of English System. Use metric system like centimer, diameter, meter, etc. while use English system such …

I recently worked on a research project where I had to find the inter-annotator agreement for tweets annotated by three annotators. Inter annotator agreement refers to the degree of agreement between multiple annotators. The quality of annotated (also called labeled) data is crucial to developing a robust statistical model. Therefore, …

It must represent a point in the Cartesian plane