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Learn to build and test a Flask-based API in Python, covering GET/POST/PUT/PATCH/DELETE requests and endpoints for efficient software communication.
Learn to create a complex input pipeline for Masked Language Modeling, transforming raw OSCAR data into a DataLoader ready for training transformer models from scratch.
Explore three vector-based approaches for language comparison and document similarity: TF-IDF, BM25, and Sentence-BERT. Learn key techniques for building effective search engines in AI and machine learning.
Explore three traditional similarity search methods: Jaccard Similarity, w-shingling, and Levenshtein distance. Learn their applications in language comparison and document matching for effective search engines.
Learn to pre-train BERT models using Next Sentence Prediction (NSP) for improved language understanding in specific domains. Practical implementation with unstructured text data.
Learn to create and publish your own Python packages on PyPI, enabling easy installation via pip. Discover the process of packaging code, structuring projects, and contributing to the Python ecosystem.
Learn to perform sentiment analysis on long texts using transformers in Python, overcoming token limitations through chunking and processing techniques for NLP tasks.
Learn to fine-tune pre-trained transformer models for question-answering using HuggingFace and PyTorch. Gain practical skills in NLP and unlock the power of transformers for custom Q&A applications.
Learn to create an interactive stock chart for GameStop using Python, PlotLy, and AlphaVantage API, exploring the coding process from concept to implementation.
Master regular expressions and their Python implementation in this comprehensive 30-minute tutorial, covering essential concepts from basic syntax to advanced techniques for efficient text processing and analysis.
Build a transformer-based sentiment classifier using HuggingFace's library in TensorFlow. Learn to download data, tokenize inputs, prepare datasets, define the model architecture, and train for multi-class sentiment analysis.
Learn to create efficient input pipelines for machine learning using TensorFlow's tf.data.Dataset API, enhancing data processing and model performance.
Learn to build a RNN text generator using TensorFlow and Python, focusing on Stoic philosophy texts. Explore key components and techniques for creating AI-generated philosophical content.
Learn to build an AI research assistant that scrapes ArXiv papers using GPT-3.5, transforming messy references into a clean format for efficient information extraction and graph-based paper organization.
Explore GPT-4's capabilities, compare it with ChatGPT, and learn to use the OpenAI Python API. Discover token limits, coding applications, and why GPT-4 is a significant advancement in AI technology.
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