Remove 2015 Remove Natural Language Processing Remove Python
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Zero-shot text classification with Amazon SageMaker JumpStart

AWS Machine Learning Blog

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. For this solution, we use the 2015 New Year’s Resolutions dataset to classify resolutions.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

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Chatbot Development using SpaCy

Heartbeat

One of the key components of chatbot development is natural language processing (NLP), which allows the bot to understand and respond to human language. SpaCy is a popular open-source NLP library developed in 2015 by Matthew Honnibal and Ines Montani, the founders of the software company Explosion.

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Comparative Analysis: PyTorch vs TensorFlow vs Keras

Pickl AI

In industry, it powers applications in computer vision, natural language processing, and reinforcement learning. Discover its dynamic computational graphs, ease of debugging, strong community support, and seamless integration with popular Python libraries for enhanced development.

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Top 10 Deep Learning Platforms in 2024

DagsHub

TensorFlow The Google Brain team created the open-source deep learning framework TensorFlow, which was made available in 2015. A good understanding of Python and machine learning concepts is recommended to fully leverage TensorFlow's capabilities. Before using Keras, ensure you have a basic understanding of Python and neural networks.

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Dead Code Should Be Buried

Explosion

Natural Language Processing moves fast, so maintaining a good library means constantly throwing things away. But most Natural Language Processing libraries do, and it’s terrible. Natural Language Processing (NLP) research moves very quickly. The new models supercede the old ones.

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sense2vec reloaded: contextually-keyed word vectors

Explosion

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. Interestingly, “to ghost” wasn’t very common in 2015.