- Top KDnuggets tweets, Mar 29 – Apr 04: Free Must-Read Books for #MachineLearning; #Apache Slug, new #BigData project - Apr 5, 2017.
Also Self-driving talent is fleeing Google and Uber to catch the autonomous-driving; Using Docker, CoreOS For #GPU Based #DeepLearning; A Short Guide to Navigating the Jupyter Ecosystem.
- Data Science Deployments With Docker - Dec 1, 2016.
With the recent release of NVIDIA’s nvidia-docker tool, accessing GPUs from within Docker is a breeze. In this tutorial we’ll walk you through setting up nvidia-docker so you too can deploy machine learning models with ease.
- Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications - Nov 10, 2016.
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
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- NVIDIA: Deep Learning Library Software Development Engineer - Oct 20, 2016.
To researchers and companies are using GPUs to power a revolution in deep learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. Join the team which is building software which will be used by the entire world.
- Neural Designer: Predictive Analytics Software - Sep 26, 2016.
Neural Designer advanced neural network algorithms, combined with a simple user interface and fast performance, make it a great tool for data scientists. Download free 15-day trial version.
- Up to Speed on Deep Learning: July Update - Aug 29, 2016.
Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.
- How to Build Your Own Deep Learning Box - Jun 2, 2016.
Want to build an affordable deep learning box and get all the required software installed? Read on for a proper overview.
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- KDnuggets™ News 16:n13, Apr 13: Signs of a Bad Data Scientist; Deep Learning from 30,000 Feet; Analytics Survey - Apr 13, 2016.
10 Signs Of A Bad Data Scientist; Deep Learning from 30,000 feet; IDC/KDnuggets Advanced Analytics Survey - please participate; A Pocket Guide to Data Science; Basics of GPU Computing for Data Scientists
- Basics of GPU Computing for Data Scientists - Apr 7, 2016.
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
- NVIDIA: Senior Data Mining Analyst - Aug 16, 2015.
Fill a key role in our Business Planning & Analytics Team, the analytic hub of NVIDIA product marketing organization.
- Popular Deep Learning Tools – a review - Jun 18, 2015.
Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.
- Top /r/MachineLearning Posts, Mar 8-14: Word vectors, Hardware for Deep Learning, and Neural Graphics Engines - Mar 19, 2015.
Word vectors in NLP, Machine Learning's place in programming, hardware for deep learning, Machine Learning interviews, and neural graphics engines are all topics covered this week on /r/MachineLearning.
- Top /r/MachineLearning Posts, Mar 1-7: Stanford Deep Learning for NLP, Machine Learning with Scikit-learn - Mar 9, 2015.
This week on /r/MachineLearning, we have a new NLP-focused deep learning course from Stanford, an introduction to scikit-learn, visualization of music collections, an implementation of DeepMind, and NLP using deep learning and Torch.
- Top /r/MachineLearning Posts, Feb 22-28: Jurgen Schmidhuber AMA and Machine Learning Done Wrong - Mar 4, 2015.
The Jürgen Schmidhuber AMA begins taking questions, machine learning done wrong, GPUs for deep learning, Google opens its native MapReduce capabilities, and Google publishes its DeepMind paper this week on /r/MachineLearning
- Facebook Open Sources deep-learning modules for Torch - Feb 9, 2015.
We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.
- Associations and Text Mining of World Events - Sep 30, 2014.
Applying frequent itemset analysis to text may seem daunting, but parallel hardware and two insights open the door to theme extraction.
- CuDNN – A new library for Deep Learning - Sep 19, 2014.
Becoming more and more popular, deep learning is proved to be useful in artificial intelligence. Last week, NVIDIA’s new library for deep neural networks, cuDNN, has attracted much attention.