Dodgy’ tax habit costing $1.5 billion Goldman Keeps Pay Ratio at 43% of Revenue – Goldman’s failure to show restraint on pay this time last year helped sow the seeds of animosity toward the industry at the end of 2009 – leading to a British bank bonus tax that is now costing the.Kenya plans guarantee scheme for home loans Mortgage lender HF Group (formerly Housing Finance Company) is seeking to offload its entire home-loans portfolio to raise cash for a fresh push into low cost housing lending, chief executive officer Sam Waweru has said. The company plans to sell its existing loans to the soon-to-be-founded kenya mortgage refinance company, which will lend banks and [.]

PyTorch is software, specifically a machine learning library for the programming language Python, based on the Torch library, used for applications such as deep learning and natural language processing. It is primarily developed by Facebook’s artificial-intelligence research group, and Uber’s Pyro probabilistic programming language software is built on it.

Scale up and out with RAPIDS and Dask Accelerated on single GPU NumPy -> CuPy/PyTorch/.. Pandas -> cuDF Scikit-Learn -> cuML Numba -> Numba RAPIDS and Others NumPy, Pandas, Scikit-Learn and many more Single CPU core In-memory dataPyData Multi-GPU On single Node (DGX) Or across a cluster Dask + RAPIDS Multi-core and Distributed PyData NumPy.

5 takeaways on industry’s health, from FDIC’s 1Q report Health. Language & region English (United States) Settings. Get the Android app.. This quite good first-quarter report, after all, follows a fiscal 2018 in which Target had its best comparable sales growth since 2005.. Key takeaways from Target’s surprisingly strong spring – StarTribune.

Specific Deep Learning VM images are available to suit your choice of framework and processor. There are currently images supporting tensorflow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows.

Probably the easiest way for a Python programmer to get access to GPU performance is to use a GPU-accelerated Python library. These provide a set of common operations that are well tuned and integrate well together. Many users know libraries for deep learning like PyTorch and TensorFlow, but there are several other for more general purpose.

Azure machine learning service users will be able to use RAPIDS in the same way they currently use other machine learning frameworks, and they will be able to use RAPIDS in conjunction with Pandas, Scikit-learn, PyTorch, TensorFlow, etc. We strongly encourage the community to try it out and look forward to your feedback!

 · [ODSC] OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow Speaker: Randy Zwitch, Senior Developer Advocate at OmniSci In this session, attendees will learn how the OmniSci GPU.

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In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we.

You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you’ll be able to implement deep learning applications in PyTorch with ease. What you will learn. Use PyTorch for GPU-accelerated tensor computations

Just a year ago, we released Kubeflow 0.1 at KubeCon Austin. Since then, the project and its community have grown significantly, both in members and contributions. As of December 18th, there are 100+.

Don’t let the grass grow “Don’t let grass grow on the path of friendship.” ~indian proverb. tags: consequences; friendship;. Don’t be upset by the results you didn’t get with the work you di. As soon as you stop wanting something, you get it. If you neglect your art for one day it will neglect you for two.