Learn how to deal with analyzing, processing text and build models that can understand the human language in Python using TensorFlow and many other frameworks.
Learn how to detect AI-generated text in Python using preprocessing, feature extraction, and a scikit-learn classifier that estimates whether a document was written by a human or a machine.
Learn to build a full-stack RAG chatbot with FastAPI, OpenAI, and Streamlit. Step-by-step guide to create a chatbot answering questions from uploaded PDFs.
Learn how to transcribe and summarize YouTube videos using Python! This tutorial covers extracting video transcripts with YouTubeTranscriptApi, processing text with NLTK, and generating AI-powered summaries using OpenRouter's Mistral model.
Learn how you can edit and style images using Instruct-Pix2Pix with the help of Huggingface diffusers and transformers libraries in Python.
Learn how you can control images generated by stable diffusion using ControlNet with the help of Huggingface transformers and diffusers libraries in Python.
Learn the current state-of-the-art models (such as BLIP, GIT, and BLIP2) for visual question answering with huggingface transformers library in Python.
Learn what is the ROUGE score and how to calculate it using rouge_score library in Python.
Learn what is Word Error Rate (WER) metric that is widely used in Automatic Speech Recognition (ASR) and how to implement it in Python along with using third-party libraries like jiwer and evaluate.
Deep-dive into the BLEU Score: A guide to understanding BLEU (Bilingual Evaluation Understudy), a vital metric in NLP. Learn how it evaluates machine-generated sentences against human references in text summarization, translation, and more with NLTK in Python.
Learn how you can fine-tune BERT or any other transformer model for semantic textual similarity using Huggingface Transformers, PyTorch and sentence-transformers libraries in Python.
Learn how you can generate similar images with depth estimation (depth2img) using stable diffusion with huggingface diffusers and transformers libraries in Python.
Learn how to perform text-to-image using stable diffusion models with the help of huggingface transformers and diffusers libraries in Python.
Learn how to use pre-trained image captioning transformer models and what are the metrics used to compare models, you'll also learn how to train your own image captioning model with Pytorch and transformers in Python.
This article discusses the preprocessing steps of tokenization, stemming, and lemmatization in natural language processing. It explains the importance of formatting raw text data and provides examples of code in Python for each procedure.
Explore different libraries for detecting natural languages such as langdetect, langid, googletrans, and language-detector in Python.
Learn how you can perform named entity recognition using HuggingFace Transformers and spaCy libraries in Python.
