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Machine learning

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Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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xorbitsai / inference

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Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.

machine-learning deployment inference pytorch artificial-intelligence llama whisper gemma mistral openai-api llm flan-t5 chatglm llamacpp vllm ggml wizardlm qwen chatglm3 llama3
  • Updated Jun 1, 2024
  • Python

SreecharanV / Building-a-Language-Understanding-and-Question-Answering-System-from-open-ended-Trivia-Data

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This project uses BERT to build a QA system fine-tuned on the SQuAD dataset, improving the accuracy and efficiency of question-answering tasks. We address challenges in contextual understanding and ambiguity handling to enhance user experience and system performance.

qa machine-learning natural-language-processing question-answering adaptive-learning machinelearning questions-and-answers squad nlp-machine-learning bert natural-language-understanding bert-model jeopardy-trivia finetuning-large-language-models
  • Updated Jun 1, 2024
  • Jupyter Notebook

bflaven / ia_usages

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An overview of the possibilities offered by artificial intelligence (AI) to serve as a technical basis for a digital product offering: from understanding, personalization, design of machine learning models and its deployment through an API built with FastAPI into the Cloud

python nlp api docker productivity machine-learning automation ai rest-api production poetry artificial-intelligence spacy datascience strategy natural-language-understanding spacy-nlp fastapi streamlit
  • Updated Jun 1, 2024
  • Python

SreecharanV / Improving-CIFAR-10-Image-Classification-with-Diverse-Architectures-Using-Ensemble-Learning

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This project uses an ensemble of CNN, RNN, and VGG16 models to enhance CIFAR-10 image classification accuracy and robustness. By combining multiple architectures, we significantly outperform single-model approaches, achieving superior classification performance.

python machine-learning machine-learning-algorithms ensemble-learning transfer-learning vgg16 ensemble-model computervision rnn-model imageclassification cnn-classification ensemble-machine-learning vgg16-model cifar10-classification
  • Updated Jun 1, 2024
  • Jupyter Notebook
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