FloydHub Blog

Practical guide to hyperparameters search for deep learning models

Learn techniques for identifying the best hyperparameters for your deep learning projects, including code samples that you can use to get started on FloydHub.

Humans of Machine Learning

Humans of ML: Dimitri Roche

The FloydHub #humansofml interview with Dimitri Roche - a software engineer who built a Shake Shack crowd-counting CoreML app using deep learning and FloydHub

Deep Learning

Asking questions to images with deep learning: a visual-question-answering tutorial

In this deep learning tutorial, we'll build Visual Question Answering (VQA) model that allows people to ask open-ended, common sense questions about the visual world.

Community

Ten Techniques Learned From fast.ai

Right now, Jeremy Howard – the co-founder of fast.ai – currently holds the 105th highest score for the plant seedling classification contest on Kaggle, but he's dropping fast. Why? His own students are beating him. And their names can now be found across the tops of leaderboards all over Kaggle.

Humans of Machine Learning

Humans of ML: Kalai Ramea

The FloydHub #humansofml interview with Kalai Ramea - a researcher at Palo Alto Research Center where she works on fundamental machine learning research involving computer vision, NLP, and big data analytics.

Humans of Machine Learning

Humans of ML: Leanne Luce

The FloydHub #HumansOfML Interview with Leanne Luce -- designer, technologist, and author of the upcoming book "Artificial Intelligence for Fashion."

Product

5 advanced features that save you time on FloydHub

FloydHub has a lot of features that accelerate various stages of your ML workflow. In this post we are sharing the 5 most useful features and how to incorporate them in to your workflow.

Inspiration

Generating Commencement Speeches With Markov Chains

Generate your own commencement speeches using Markov chains in Python on FloydHub.

Inspiration

Automate FloydHub with Celery

Jelle Hoffman from Ubidots shows how he used Celery to automate their FloydHub api management

Announcement

Workspaces on FloydHub

Meet Workspaces 👋- a new cloud IDE for deep learning, powered by FloydHub GPUs

Product

Metrics on FloydHub

At FloydHub we offer cutting edge infrastructure for training your machine learning projects. These are highly tuned instances in the cloud that come pre-installed with the frameworks of your choice. With the machines

Product

FloydHub for Teams

We're launching a Teams plan on FloydHub today. You can now have a secure, centralized hub for your entire data science team's model development, training, and deployment pipeline, along with roles-based permissions, consolidated billing, and even more collaboration magic.

Inspiration

Teaching My Robot With TensorFlow

If you're like me, then you'd do pretty much anything to have your own R2-D2 or BB-8 robotic buddy. Just imagine the adorable adventures you'd have together! I'm delighted to report that the

Deep Learning

Turning Design Mockups Into Code With Deep Learning

Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

Deep Learning

Benchmarking FloydHub instances

This post compares all the CPU and GPU instances offered by FloydHub, so that you can choose the right instance type for your training job. Benchmark For our benchmark we decided to use

Deep Learning

Checkpointing Tutorial for TensorFlow, Keras, and PyTorch

Ready to build, train, and deploy AI? Get started with FloydHub's collaborative AI platform for free Try FloydHub for free This post will demonstrate how to checkpoint your training models on FloydHub so

Product

Creating Datasets from Public URLs

We're trying to make it easier to discover and use interesting datasets on FloydHub. To that end, we just released an improved file-viewer to help you dig in and explore data directly on

Inspiration

Generating Six-Pack Abs With TensorFlow pix2pix

Visualizing results can be a powerful form of motivation and preparation. However, in the fitness domain, it can often be difficult to clearly see this future outcome. Can we use deep learning to

Deep Learning

Colorizing B&W Photos with Neural Networks

I’ll show you how to build your own colorization neural net in three steps. The first section breaks down the core logic. We’ll build a bare-bones 40-line neural network as an “Alpha" colorization bot.

Deep Learning

Building Your First ConvNet

Convolutional Neural Networks (ConvNets) are increasingly popular, and for all the right reasons. ConvNets have the unique property of retaining translational invariance. In elaborative terms, they exploit spatially-local correlation by enforcing a local

Announcement

Restart Jupyter Notebook Workflow

We snuck in a new feature that should make your Jupyter notebook workflow on FloydHub oh-so-much easier - the restart button. Now you can spin up a Jupyter notebook from the FloydHub web

Deep Learning

Getting Started with Deep Learning on FloydHub

This post is aimed at helping new users (especially the ones who are starting out & cannot afford Andrej Karpathy’s rig) setup an on-the-go deep learning solution for their small to medium

Deep Learning

Coding the History of Deep Learning

There are six snippets of code that made deep learning what it is today. This article covers the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with.

Product

Should I buy my own GPUs for Deep Learning?

Deep learning algorithms involve huge amounts of matrix multiplications and other operations which can be massively parallelized. GPUs usually consist of thousands of cores which can speed up these operations by a huge

Deep Learning

My First Weekend of Deep Learning

The current wave of deep learning took off five years ago. Exponential progress in computing power followed by a few success stories created the hype. It’s the technology that drives cars, beats humans in Atari games, and diagnoses cancer.

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