Slack is one of the fastest growing companies of all time, and there’s a good chance it’s also the messaging app that you use for work. The Algorithmia Slack Client lets you integrate Machine Learning into your Slack channels – both as slack commands and as bot users – giving you more firepower on top of the already vibrant ecosystem of third party Slack integrations.
Despite only making it into the political mainstream recently, crowd size estimation has always been an important task for corporate development, retail planning, and resource allocation. It helps property owners and event organizers predict demand, understand utilization of physical locations, and test different product launches and arrangements. And Machine Learning is making it more accessible than ever.
Machine learning is about rapid experimentation and iteration, and without keeping track of your modeling history you won’t be able to learn much. Versioning lets you keep track of all of your models, how well they’ve done, and what hyperparameters you used to get there. This post will walk through why data versioning is important, tools to get it done with, and how to version your models that go into production.
Anyone who is interested in deep learning has likely gotten their hands dirty at some point playing around with Tensorflow, Google’s open source deep learning framework. Tensorflow has a lot of benefits like wide-scale adoption, deployment on mobile, and support for distributed computing, but it also has a somewhat challenging learning curve, and is difficult to debug. It also doesn’t support variable input lengths and shapes due to its static graph architecture unless you use external packages. PyTorch is a new deep learning framework that solves a lot of those problems.
PyTorch is only in beta, but users are rapidly adopting this modular deep learning framework. PyTorch supports tensor computation and dynamic computation graphs that allow you to change how the network behaves on the fly unlike static graphs that are used in frameworks such as Tensorflow. PyTorch also offers modularity, which enhances the ability to debug or see within the network. For many, PyTorch is more intuitive to learn than Tensorflow.
This talk will objectively look at PyTorch and why it might be the best fit for your deep learning use case. We’ll look at use cases that will showcase why you might want consider using Tensorflow instead.
User experience and customer support are integral to every company’s success. But it’s not easy to understand what users are thinking or how they are feeling, even when you read every single user message that comes in through feedback forms or customer support software. With Natural Language Processing and Machine Learning techniques it becomes somewhat easier to understand trends in user sentiment, main topics discussed, and detect anomalies in user message data.
A couple of weeks ago, we gave a talk about investigating user experience with natural language analysis at Sentiment Symposium and thought we’d share the talk, along with the speaker notes for anyone who is interested.