Industry News

Industry News

JupyterLab-best open source software for data storage and analytics; Keys for Cloud-Based Machine Learning; San Diago workshop for Data Wrangling and Cleaning; Tutorial on Anaconda, NumPy and Pandas; Sleep Stage Classification; Set Up The AI Development Environment With Tensorflow, Exploratory Data Analysis; Chatbots

JupyterLab and Notebook News. 02, October 2018

News clips provided algorithmically.

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The best open source software for data storage and analytics

InfoWorld’s 2018 Best of Open Source Software Award winners in databases and data analytics

JupyterLab, the next generation of Jupyter, the venerable web-based notebook server beloved by data scientists everywhere. …
2019-09-27 00:00:00 Read the full story.

CloudQuant Thoughts: We aren’t surprised by JupyterLab winning this award. We are using JupyterLab internally for our researchers. This post was found using a python script running inside a JupyterLab environment that searches for  posts that we find interesting and want to share with you.
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The Key Factor That Influences The Adoption Of Cloud-Based Machine Learning Platforms

Cloud computing has influenced the rise of machine learning and artificial intelligence. Factors such as affordable storage, availability of GPUs and FPGAs and advancements in deep learning made machine learning accessible and affordable to businesses.
Mainstream cloud providers have shifted their focus from pushing traditional IaaS to selling PaaS based on machine learning. Cognitive APIs, automated ML, model management and preconfigured data science VMs backed by GPUs are going to…
2018-10-01 09:00:00 Read the full story.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

San Diego Workshop Tackles Data ‘Wrangling’ and ‘Cleaning’

Artificial intelligence and machine learning may be the focus of popular and media attention, but data scientists spend most of their time “wrangling” and “cleaning” data so that computers can produce useful information.
A public workshop on Tuesday, Oct. 2, will offer insights into this tedious but fundamental challenge. Thomas Donoghue of UC San Diego’s Department of Cognitive Science will explain the concepts behind data wrangling and cleaning — getting data loaded and checking it for quality.
2018-10-02 Read the full story.
CloudQuant Thoughts: Short Notice – but you should go if you can.

My Tutorial Book on Anaconda, NumPy and Pandas Is Out: Hands-On Data Analysis with NumPy and Pandas

I announced months ago that one of my video courses, Unpacking NumPy and Pandas, was going to be turned into a book. Today I’m pleased to announce that this book is available!
Hands-On Data Analysis with NumPy and Pandas is now available for purchase from Packt Publishing’s website and from Amazon. This book was created by a team at Packt Publishing who took my video course and turned it into book form. If you’re like me and love books that you …
2018-10-01 00:00:00 Read the full story.

Sleep Stage Classification from Single Channel EEG using Convolutional Neural Networks

Quality Sleep is an important part of a healthy lifestyle as lack of it can cause a list of issues like a higher risk of cancer and chronic fatigue. This means that having the tools to automatically and easily monitor sleep can be powerful to help people sleep better.
Doctors use a recording of a signal called EEG which measures the electrical activity of the brain using an electrode to understand sleep stages of a patient and make a diagnosis a…
2018-10-01 21:13:36.550000+00:00 Read the full story.

How To Set Up The AI Development Environment For The First Time With Tensorflow

Building algorithmic agents with neural networks is the go-to business strategy in the current technology environment. Now, Google’s Tensorflow library helps developers build these agents with pre-defined functions for easy implementations of various tasks. In this article, we shall be going through the steps to setup an environment for development of these models with Tensorflow library. Setting up an environment for these tasks is mandatory because each model you build is unique to one another and have different dependencies.
Install …
2018-09-24 05:36:43+00:00 Read the full story.

Hitchhiker’s guide to Exploratory Data Analysis – Towards Data Science

movies_df.head() is going to display the first 5 rows of the dataframe. You can pass the number of rows you want to see to the head method. Take a look at the dataframe we’ve got:
Here, I’ve used pandas’ read_csv function which returns a fast and efficient DataFrame object for data manipulation with integrated indexing. I’ve two dataframes from movies_df and credits_df.
Import the python packages which you would need to clean, crunch and visual…
2018-10-02 00:52:42.122000+00:00 Read the full story.

A Chatbot from Future: Building an end-to-end Conversational Assistant with

A Chatbot from Future: Building an end-to-end Conversational Assistant with
You might have seen in my previous post that I’ve been using to build chatbots. You will find many tutorials on Rasa that are using Rasa APIs to build a chatbot. But I haven’t found anything that talks details on those APIs, what are the different API parameters, what do those parameters mean and so on. In this post, I will not only share how to build a c…
2018-10-01 20:55:33.185000+00:00 Read the full story.

Help! I can’t reproduce a machine learning project!

Have you ever sat down with the code and data for an existing machine learning project, trained the same model, checked your results… and found that they were different from the original results?
Not being able to reproduce someone else’s results is super frustrating. Not being able to reproduce your own results is frustrating and embarrassing. And tracking down the exact reason that you aren’t able to reproduce results can take ages; it took me…
2018-09-19 00:00:00 Read the full story.

IPython 7.0, Async REPL – Jupyter Blog

IPython 7.0, Async REPL
Today we are pleased to announce the release of IPython 7.0, the powerful Python interactive shell that goes above and beyond the default Python REPL with advanced tab completion, syntactic coloration, and more. It’s the jupyter kernel for python used by millions of users, hopefully including you. This is the second major release of IPython since we stopped support for Python 2.
Not having to support Python 2 allowed us …
2018-09-27 17:41:03.848000+00:00 Read the full story.


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