Transfer-learning Libraries

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Transfer-learning Libraries

:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

This command installs everything needed for basic Pipelines that use an in-memory DocumentStore. If you want to install only the dependencies needed for model inference on your local hardware (not re…

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!

This is an open-source project welcoming your contributions. You can contribute in three ways: The development of PyKale is partially supported by the following project(s).

Chest Xray image analysis using Deep learning !

Finally the saved models are used for making predictions.Confusion Matrix is used as the Performance Metrics for this classifcation task.

Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!

All of the Keras built in models are made available: This project has the following dependencies: The only thing you have to do to get started is set up the folders in the following structure:

CNN image classifier implemented in Keras Notebook 🖼️.

Check out corresponding Medium article: Structure your data as follows: For binary classifications you are good to go! For non-binary classifications:

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a han…

Almost any Image classification problem using pytorch

To train the models: To test the models: total number of train images: 4268 total number of val images: 482 total number of test images: 794

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.