Optimizing CUDA Recurrent Neural Networks with TorchScript
To get started, you can use this file as a template to write your own custom RNNs.
We are constantly improving our infrastructure on trying to make the performance better. If you want to gain the speed/optimizations that TorchScript currently provides (like operator fusion, batch matrix multiplications, etc.), here are some guidelines to follow. The next section explains the optimizations in depth.
Bring your Jupyter Notebook to life with interactive widgets
Traditionally, every time you need to modify the output of your notebook cells, you need to change the code and rerun the affected cells. This can be cumbersome, inefficient and error prone and in the case of a non-technical user it may even be impracticable.
10x Faster Parallel Python Without Python Multiprocessing
While Python's multiprocessing library has been used successfully for a wide range of applications, in this blog post, we show that it falls short for several important classes of applications including numerical data processing, stateful computation, and computation with expensive initialization.
The reason I am using Altair for most of my visualization in Python
Sadly, in Python, we do not have a ggplot2.
Python's go to visualization library, matplotlib, is very powerfulmatplotlib recently came into the spotlight again for being attributed the first black hole image.
but has severe limitations. At times its flexibility is a blessing, but it is easy to get frustrated adding a small feature to your graph. Also, matplotlib dual object oriented and state-based interface is confusing. I still don't completely grasp it even though I have been using matplotlib for years. Lastly, it is not easy to make interactive charts.
Extreme Rare Event Classification using Autoencoders in Keras
In a rare-event problem, we have an unbalanced dataset. Meaning, we have fewer positively labeled samples than negative. In a typical rare-event problem, the positively labeled data are around 5--10% of the total. In an extreme rare event problem, we have less than 1% positively labeled data. For example, in the dataset used here it is around 0.6%.
Tutorial: Text Analysis in Python to Test a Hypothesis
People often complain about important subjects being covered too little in the news. One such subject is climate change. The scientific consensus is that this is an important problem, and it stands to reason that the more people are aware of it, the better our chances may be of solving it. But how can we assess how widely covered climate change is by various media outlets? We can use Python to do some text analysis!
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
Posted by Julien Valentin and Sofien Bouaziz
from TensorFlow
Github repository: https://github.com/tensorflow/graphics
The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these new layers leverage the knowledge acquired over years of computer vision and graphics research to build new and more efficient network architectures.
How to get started with Python for Deep Learning and Data Science
You can code your own Data Science or Deep Learning project in just a couple of lines of code these days. This is not an exaggeration; many programmers out there have done the hard work of writing tons of code for us to use, so that all we need to do is plug-and-play rather than write code from scratch.
How back-propagation works, and how you can use Python to build a neural network
Neural networks can be intimidating, especially for people new to machine learning. However, this tutorial will break down how exactly a neural network works and you will have a working flexible neural network by the end. Let's get started!
We're All Using Airflow Wrong and How to Fix It --- ( Personal Favorite!)
Tl;dr: only use Kubernetes Operators
At Bluecore, we have been investing in Airflow as our primary workflow management tool. After a few months of work and promoting the platform internally, we found that we still had low adoption across the team.
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