Valohai Blog

Reinforcement Learning Tutorial Part 1: Q-Learning

[fa icon="calendar'] Jan 25, 2019 1:40:45 PM / by Juha Kiili posted in Tutorial, Reinforcement Learning, Q-learning

This is the first part of a tutorial series about reinforcement learning. We will start with some theory and then move on to more practical things in the next part. During this series, you will not only learn how to train your model, but also what is the best workflow for training it in the cloud with full version control using the Valohai deep learning management platform.

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Run Jupyter Notebook On Any Cloud Provider

[fa icon="calendar'] Dec 20, 2018 8:50:33 AM / by Juha Kiili posted in Jupyter Notebook, Machine Learning Tools

This tutorial will demonstrate how to take a single cell in a local Jupyter Notebook and run it in the cloud, using the Valohai platform and its command-line client (CLI).

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Random hyperparameter optimization

[fa icon="calendar'] Dec 12, 2018 2:57:30 PM / by Aarni Koskela posted in New Features, Product Updates

Valohai now supports random search for hyperparameter optimization (which we call the Tasks feature), which has been proven in the aptly named paper Random search for hyper-parameter optimization to be an efficient way to find “neighborhoods” of likely-to-be-optimal hyperparameter values, which can then be iterated further to find the really good values.

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The Journey from Deep Learning Experimentation to Production-Ready Model Building

[fa icon="calendar'] Dec 3, 2018 11:10:57 AM / by Fredrik Rönnlund posted in Deep Learning in Production

Since the rise of the deep learning revolution, springboarded by the Krizhevsky et al. 2012 ImageNet victory, people have thought that data, processing power and data scientists were the three key ingredients to building AI solutions. The companies with the largest datasets, the most GPUs to train neural networks on, and the smartest data scientists were going to dominate forever.

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Watch the Webinar on Version Control in Machine Learning

[fa icon="calendar'] Nov 27, 2018 12:22:34 PM / by Joanna Purosto posted in Webinar, Version Control, Machine Learning

Watch a recording of the webinar on version control in machine learning that was held on 22th of November 2018. During the webinar we discussed about the topics below and answered multiple questions addressed by the attendees. 

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PocketFlow with Valohai

[fa icon="calendar'] Nov 26, 2018 2:18:00 AM / by Juha Kiili

PocketFlow is an open-source framework from Tencent to automatically compress and optimize deep learning models. Especially edge devices such as mobile phones or IoT devices can be very limited on computing resources so sacrificing a bit of model performance for a much smaller memory footprint and lower computational requirements is a smart tradeoff.

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Microsoft's Cognitive Toolkit (CNTK) on Valohai

[fa icon="calendar'] Nov 21, 2018 11:28:51 AM / by Eero Laaksonen posted in Microsoft, CNTK

Microsoft's Cognitive Toolkit or CNTK is an open source framework for building Deep Learning models. This relatively new framework has been gaining traction so we decided to make sure Valohai supports it well. One of the benefits over competing frameworks has been CNTK’s ground up support for multi-node, multi-GPU training, something that for instance TensorFlow has been struggling to tackle well. If you are doing work on really large datasets, you should maybe give it a try.

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Synthetic Training Dataset with Unity

[fa icon="calendar'] Nov 13, 2018 1:23:29 PM / by Ruksi posted in Unity, Machine Learning Tools

Synthetic data is artificially created information rather than recorded from real-world events. A simple example would be generating a user profile for John Doe rather than using an actual user profile. This way you can theoretically generate vast amounts of training data for deep learning models and with infinite possibilities.

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GDPR and its Effects on Machine Learning Based Decisions

[fa icon="calendar'] Oct 31, 2018 8:23:56 AM / by Joanna Purosto posted in GDPR+ML

You might have heard that every individual subject to automated decision making by machine learning models has a right to an explanation of the result. I bet you feel drops of sweat forming on your forehead when you receive an inquiry from a manager saying that he needs details about how a certain decision was made. If thinking about this scenario gives you chills, you are in the right place. Read further and learn how to tackle the transparency issue.

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What to Store from a Machine Learning Experiment

[fa icon="calendar'] Oct 23, 2018 8:46:02 AM / by Eero Laaksonen posted in Machine Learning Experiment, Machine Learning Infrastructure

When meeting with teams that are working with machine learning today, there is one point above everything else that I try to teach. It is the importance of storing and versioning of machine learning experiments and especially how many things there actually are that need to be stored.

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