Malte Pietsch delivers this keynote on “Transfer Learning – Entering a new era in NLP” at PyData Warsaw 2019
Transfer learning has been changing the NLP landscape tremendously since the release of BERT one year ago. Transformers of all kinds have emerged, dominate most research leaderboards and have made their way into industrial applications. In this talk we will dissect the paradigm of transfer learning and its effects on pipelines, modelling and the engineers mindset.
Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.
Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!
PyOhio posted this great talk by Alice Zhao on NLP in Python.
Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I’m working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP.
Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning.
We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.
Siraj Raval gets back to inspiring people to get into AI and pokes fun at himself.
Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that’s not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!
On this episode of Talk Python, meet Francesca Lazzeri and hear story how she went from Research Fellow in Economics at Harvard Business School to working on the AI and data science stack on the Azure team.
Here’s a great walkthrough of how to create a docker image that exclusively uses a custom model to run predictions and returns back the result.
This provides context isolation for the application running the model and avoids any non-reproducibility issue.
When dealing with python versioning you can easily get lost with all the possible combinations of your favorite library version, and the versions of the interpreter. Many different projects, specially the ones involving Tensorflow and Keras, require a very specific library version to run. Many other projects are not […]