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Search: python-training
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Showing 10 results from 382
Azure-Samples/MLOpsDatabricks
GitHub Python MIT LicenseMLOps practices using Azure ML service with Python SDK and Databricks for model training
External source
GitHub
KTS-o7/better_bing_image_downloader
GitHub Python MIT LicensePython library + CLI to bulk-download images from Bing or DuckDuckGo. Embeddable API, async, typed errors, JSONL manifest for ML training data. Good first issues available.
External source
GitHub
talkpython/python-memory-management-course
GitHub Python MIT LicenseDemo code exploring Python's memory models and collection algorithms from the Talk Python Training course.
External source
GitHub
cindysridykhan/instruct_storyteller_tinyllama2
GitHub Jupyter NotebookTraining and Fine-tuning an llm in Python and PyTorch.
External source
GitHub
LinkedInLearning/Neural-Networks-Python-2851003
GitHub Python OtherTraining Neural Networks in Python
External source
GitHub
WangXiaoMingo/TensorDL-MPC
GitHub Python GNU Affero General Public License v3.0DL-MPC(deep learning model predictive control) is a software toolkit developed based on the Python and TensorFlow frameworks, designed to enhance the performance of traditional Model Predictive Control (MPC) through deep learning technology. This toolkit provides core functionalities such as mode... Read more
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GitHub
mudigosa/Image-Classifier
GitHub Jupyter Notebook MIT LicenseImage Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overa... Read more
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GitHub
stephrobert/python-training
GitHub Python Creative Commons Attribution Share Alike 4.0 InternationalUne formation Python pour les Admin Sys
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GitHub
stanfordnlp/stanza-train
GitHub PythonModel training tutorials for the Stanza Python NLP Library
External source
GitHub
jingweili87/DeepECG-Reconstructor
GitHub PythonPython framework for designing and training Deep Learning models to reconstruct 12-lead Electrocardiograms from 3-lead inputs and classify clinical conditions effectively.
External source
GitHub
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