Resources for developers using Python for scientific computing and quantitative analysis
Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem.
Using transfer learning can dramatically speed up the rate of deployment for an app you are designing, making both the training and implementation of your deep neural network simpler and easier.
Hugging Face, the NLP startup behind several social AI apps and open source libraries such as PyTorch BERT, just released a new python library called PyTorch Transformers.
Transformers are a new set of techniques used to train highly performing and efficient models for performing natural language processing (NLP) and natural language understanding (NLU) tasks such as questions answering and sentiment analysis. Several of the recent techniques used to improve and advance the performance of NLP models, such as XLNet and BERT, are all based on a variation of Transformer.
PyTorch is the fastest growing Deep Learning framework and it is also used by Fast.ai in its MOOC, Deep Learning for Coders and its library. PyTorch is also very pythonic, meaning, it feels more natural to use it if you already are a Python developer.
Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.
