JupyterLab and Jupyter Notebooks

CloudQuant uses JuypterLab and Jupyter Notebooks in all of our data science projects. We find that this popular python environment with the standard and extended packages allow our internal and external quantitative analysts to create and share research that includes python code, equations, visualizations and research text. Our new CloudQuant.AI environment which is only available for internal users is built on JupyterLab. Watch for announcements from CloudQuant as we plan on opening this platform with our market data, fundamental data, and news/sentiment Alternative Datasets for registered crowd researchers.

The Jupyter Project is an open source project and can be found at http://jupyter.org/


Python Plotly Candlestick Chart with annonations

Candlestick Charts in Python with Plotly

Some traders are visually oriented. They need charts. As data scientists, we need to be able to present information in a way that others can understand. Presenting traders a candlestick chart is one of the best ways to transfer useful data. Blog Purpose: ✅ Demonstrate how to create a basic candlestick chart in Python 3 ✅ Demonstrate how to highlight/annotate points on the chart Topics covered in this post: Python, Plotly, OHLC, Candlestick Charts, Jupyter, Pandas, Traders
Numpy Memory Leak Fix

Fixing Python Memory Leaks

A few of our power users reported that long-running backtests would sometimes run out of memory. These power-users are the people who often find new trading strategies and so we wanted to work with them to improve the performance of our backtesting tools. Over the past couple of weeks, our senior engineer found that the problem wasn’t in our code, but in one of the popular Python libraries that we use. We found the problem in numpy and numba. 

JupyterLab and Notebook News. 25, October 2018

News clips about JupyterHub, JupyterLab, and Jupyter Notebooks provided algorithmically.

How to Deploy JupyterHub with Kubernetes on OpenStack

Deploying JupyterHub with Kubernetes on OpenStack Jupyter is now widely used for teaching and research. The use of Kubernetes for deploying a JupyterHub has enabled reliable setups scaling to thousands of users. There are many cloud computing vendors (Google, Amazon, …) and the first attempts to use JupyterHub with Kubernetes is based on them. But relying on vendor clouds increases the risk of vendor lock-in. In addition, there are many pre-ex… 2018-10-15 13:11:34.092000+00:00 Read the full story.

How to Export Jupyter Notebooks into Other Formats

When working with Jupyter Notebook, you will find yourself needing to distribute your Notebook as something other than a Notebook file. The most likely reason is that you want to share the content of your Notebook to non-technical users that don’t want to install Python or the other dependencies necessary to use your Notebook. The most popular solution for exporting your Notebook into other formats is the built-in nbconvert tool. You can use nbco… 2018-10-09 00:00:00 Read the full story.

Testing Jupyter Notebooks

The more you do programming, the more you will here about how you should test your code. You will hear about things like Extreme Programming and Test Driven Development (TDD). These are great ways to create quality code. But how does testing fit in with Jupyter? Frankly, it really doesn’t. If you want to test your code properly, you should write your code outside of Jupyter and import it into cells if you need to. This allows you to use Python’s … 2018-10-16 00:00:00 Read the full story.

Predicting the Stock Market Using Machine Learning and Deep Learning

Introduction Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. physhological, rational and irrational behaviour, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy. Can we use machine learning as a game changer in this domain? Using features like the latest annou… 2018-10-25 08:38:44+05:30 Read the full story.

Google’s New Machine Learning Curriculum Aims to Stop Bias Cold

Google loves machine learning (ML). Now, it’s launched a new course module that aims to help you, a human, recognize your own bias before training ML models. Named ‘Fairness,’ the course is 70 minutes on how humans are compromising machine learning models. From Google: As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected b… 2018-10-24 00:00:00 Read the full story.

Airbnb details its journey to AI-powered search

Online booking platform Airbnb has more than 5 million property listings and tens of thousands of tours, hikes, and other travel experiences on offer. That’s a lot for anyone to sift through, but the San Francisco startup believes that artificial intelligence (AI) can lend a hand. In a paper published on the preprint server Arxiv.org (“Applying Deep Learning To Airbnb Search“), researchers at the company describe how over the course of two years… 2018-10-24 00:00:00 Read the full story.

Azure Content Spotlight – Get started with developing AI applications

Welcome to another Azure Content Spotlight! These articles are used to highlight items in Azure that could be more visible to the Azure community. This weeks content spotlight is all for developers that want to get started with developing AI applications or more experienced AI developers that want expand their knowledge. AI is a set of technologies that enable computers to assist and solve problems in a way that are similar to humans by perceiv… 2018-10-25 00:00:00 Read the full story.

PyDev of the Week: Marc Garcia

This week we welcome Marc Garcia (@datapythonista) as our PyDev of the Week! Marc is a core developer of pandas, a Python data analysis library. If you’d like to know more about Marc, you can check out his website which has links to his talks that he has given at PyData in Europe as well as talks at EuroPython. In fact, here is one of his talks on pandas in case you are interested: You can also see what projects he is a part of over on Github. … 2018-10-15 00:00:00 Read the full story.  
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.

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. quantitative algo trading conceptual dashboard

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 Rasa.ai

A Chatbot from Future: Building an end-to-end Conversational Assistant with Rasa.ai You might have seen in my previous post that I’ve been using Rasa.ai 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.  
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
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Conversations: Learning Python within CloudQuant

What was your experience like learning Python within CloudQuant?

We asked our portfolio managers and product management teammates who code in Python to explain their starting experiences in programming with Python with CloudQuant. We wanted to share with everyone what encouraged them to keep learning throughout the years. Everyone here codes as part of their job. This includes the CEO all the way down to the interns. We rely on our Backtesting Engine to ensure that trading algorithms work well before committing money to the automated trading strategies. But we also use JupyterLab in our daily work. We generate our reports, monitor our systems, and do all sorts of tasks in Python. Python has overtaken the spreadsheet in CloudQuant. The teammates featured in this video are:
  • Morgan Slade- CEO
  • Tayloe Draughon- Senior Product Manager
  • James Chang- Quantitative Portfolio Manager
  • Rob Ferguson- Quantitative Equity Portfolio Manager
  • Simon Zhang- Quantitative Analyst
Watch this video to gain insight as to what got our amazing coders to where they are.

JupyterLab and Notebook News. 08, June 2018

News clips provided algorithmically.

Looker Boosts Data Science Capabilities in Latest Platform Release

Looker, a data platform provider, is releasing new tools and integrations to optimize data science workflows. Looker is improving its governed data workflow with an SDK for R and connections for Python, as well as streamed and merged results, Google TensorFlow integrations, and clean, visual recommendations for users. “We’re focused on preparation that happens which a lot of research will tell you takes up 70-80% of data scientists time and the… 2018-05-30 00:00:00 Read the full story.

Unsupervised Deep Learning Algorithms for Computer Vision

Introduction It is often said that in machine learning (and more specifically deep learning) – it’s not the person with the best algorithm that wins, but the one with the most data. We can always try and collect or generate more labelled data but it’s an expensive and time consuming task. This is where the promise and potential of unsupervised deep learning algorithms comes into the picture. They are designed to derive insights from the data wi… 2018-06-07 00:17:56+05:30 Read the full story.

BlueData Introduces Turnkey Solution for AI and Machine Learning

The BlueData AI/ML Accelerator solution includes software and professional services to deploy containerized multi-node sandbox environments for exploratory use cases with TensorFlow and other ML/DL tools. According to BlueData, AI and ML/DL have moved into the mainstream with a broad range of data-driven enterprise applications such as credit card fraud detection, stock market prediction for financial trading, credit risk modeling for insurance, genomics and precision medicine, disease detection and diagnosis, natural language processing (NLP) for customer service, autonomo… 2018-05-31 00:00:00 Read the full story.    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
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AI & Machine Learning News. 28, May 2018

 ForwardX raises $10 million for AI-powered luggage that follows you

Autonomous luggage maker ForwardX Robotics today announced it has raised $10 million to bring its suitcase Ovis to market. At $399, the luggage can move a maximum 6.2 miles per hour and will ship to its first customers in late 2018. ForwardX was founded in 2016, but its luggage initially grabbed the world’s attention in January at the Consumer Electronics Show (CES) 2018 in Las Vegas. The 9.9 lb suitcase is made of polypropylene and carbon fiber and is able to follow you by deploying computer vision that tracks your body and face, even if you are momentarily out of sight. Though Ovis has been tested and found to be useful in environments outside airports, like city streets, its battery only lasts for four hours of use, and it must be switched to the old-fashioned manual mode on escalators since it cannot yet handle moving stairs. 2018-05-25 00:00:00 Read the full story. CloudQuant Thoughts… Been there, Done that! Seriously, have you seen the way they treat your luggage!?  

Chinese schools are testing AI that grades papers almost as well as teachers

Some schools in China have incorporated paper-grading artificial intelligence into their classrooms, according to the South China Morning Post. One in every four schools, or about 60,000 institutions, are quietly testing a machine learning-powered system that can score students’ work automatically, and even offer suggestions where appropriate. The AI, which can be accessed through various online portals, and which the report describes as similar to the system used by the Education Testing Service in the U.S., uses an evolving “knowledge base” to interpret the “general logic” and “meaning” of pupils’ essays and to highlight stylistic, structural, and thematic areas that need improvement. It can read both English and Chinese, and it’s reportedly perceptive enough to notice when paragraphs veer too far off topic. 2018-05-28 00:00:00 Read the full story. CloudQuant Thoughts… But who will you go to if you want to dispute your grade? Seriously, as long as the direction of the education is not interfered with, anything that frees up teachers time will benefit all students.  

Understanding the Trust Factor in the Analytics Era

Today, data is increasingly seen as the fuel of the business, rather than its byproduct. As a result, the old adage “garbage in, garbage out” couldn’t ring truer when it comes to maximizing the value of machine learning in the enterprise, according to Steve Zisk, senior product marketing manager of RedPoint Global, which provides a customer data platform and engagement hub. Zisk presented a session at Data Summit 2018, titled “Garbage in, Garbage Out: Why Data Quality Is the Lifeblood of Machine Learning.” Machine learning is worthless if it’s fueled by bad data, according to Zisk, who covered why simply collecting massive amounts of data isn’t enough to extract value from machine learning technology; what’s real and what’s hype when it comes to machine learning; and best practices for using machine learning to predict, identify patterns, and optimize processes for reaching customers effectively. 2018-05-24 00:00:00 Read the full story. CloudQuant Thoughts… A lot of people assume ML and AI are the panaceas for all their problems, “just throw ML at it”. But the quality of the data is paramount, as is the structure of the data. In the trading environment huge amounts of data are derived from a simple update of the Bid/Ask or a new Trade – two events of less than 50 characters of data each.    

Pillars of a Hybrid Data Management Strategy: Hybrid, Flexible and Cognitive

Chris Reuter, North America data warehousing sales leader, IBM, presented a keynote at Data Summit 2018 on the key trends in IT today and what organizations must do to advance their organizations.   Reuter focused on three main themes in the marketplace:
  • According to IDC, data will see a 30% CAGR through 2025 – that means you have think about how over the next 7 years you are going to deal with 30% CAGR on data. Cognitive systems themselves create all kinds of data and metadata so you have got to be able to store, analyize and govern that data.
  • According to McKinsey, investment growth in AI has increased 3x since 2013. Getting actual projects into production is hard but we need to infuse cognitive computing and AI into the data, he noted.
  • According to Gartner, there is value in all approaches to cloud, whether the strategy is pure cloud or not, depending on the enterprise and end goals.
2018-05-24 00:00:00 Read the full story. CloudQuant Thoughts… A 30% Cumulative Annual Growth Rate (CAGR) on data is staggering. We agree wholeheartedly with the Essential Elements and are aiming to make the first Hybrid Trading Data Management system in CloudQuant AI very soon.  

AWS’s Recently-Launched Features ‘Transcribe’ And ‘Translate’ Are Using Machine Learning In A Revolutionary Manner

Last month at AWS re:INVENT developers conference, Amazon announced two new services — Amazon Transcribe and Amazon Translate — with an aim to improve the company’s artificial intelligence and machine learning capabilities. Amazon Transcribe can analyse audio files (.wav, .mp3, .flac) stored on Amazon S3. On the other hand, Amazon Translate provides a fast translation of text-based content to create a multilingual experience on the web. It can also simply mass-translate documents on command. 2018-05-28 08:52:29+00:00 Read the full story. CloudQuant Thoughts… There are a number of similar services already available that you can try with your own Python scripts. Python (which we use in CloudQuant) is an easy to learn yet powerful language.  

The 4 Machine Learning Skills You Won’t Learn in School or MOOCs

Machine Learning (ML) has become massively popular over the last several years. And why… well simply because it works! The latest research has achieved record breaking results, even surpassing human performance on some tasks. Of course as a result many people are rushing to get into this field; and why not. It’s well funded, the technology is exciting and interesting, and there’s lots of room for growth. 2018-05-28 16:45:55.752000+00:00 Read the full story. CloudQuant Thoughts… Your model must fit the ‘business requirements’. You must ‘quickly’ select the correct ML modelling type. You MUST fit your model into the current system (no point in building a model that receives yesterday’s market data and takes three hours to crunch if you only get the data two house before the next day opens!). Strive to get the most bang for your buck. For me, all of these fit the classic 80/20 rule.  

The one essential skill that will set you apart from other developers

…and how you can hone this skill in five easy ways. According to the Global Developer Population and Demographic Study conducted by Evans Data Corporation, there are over 22 million developers worldwide and this figure is expected to raise to 26 million in 2022. So. Many. Developers. If you are one of the 20 something millions developers in the world, you might be wondering how you can set yourself apart from others and stand out from the crowd. Today, I’d like to share with you the one essential skill that is most valued for developers but not every developer possesses or understands its importance. And no, it is not public speaking skill. It took me a while to come up with an appropriate label for this skill, but I have finally come up one that I am happy with. This essential skill is the ability to “Think and act like a CEO”. 2018-05-28 07:31:01.204000+00:00 Read the full story. CloudQuant Thoughts… This is a really nice article, the final skill is often overlooked and often what kills many a young company.  

Beginner’s Guide to Jupyter Notebooks for Data Science (with Tips, Tricks!)

One of the most common questions people ask is which IDE / environment / tool to use, while working on your data science projects. As you would expect, there is a wealth of options available – from language specific IDEs like R Studio, PyCharm to editors like Sublime Text or Atom – the choice can be intimidating for a beginner. If there is one tool which every data scientist should use or must be comfortable with, it is Jupyter Notebooks. 2018-05-24 11:46:47+05:30 Read the full story. CloudQuant Thoughts… Our next product, CloudQuant.AI leverages Jupyter Notebooks into a streamlined IDE in a webpage. We will bring you more news soon.  
Below The Fold…

Enabling the Real Time Enterprise with Data Lakes, Streaming Data, and the Cloud

oduct management and marketing, Attunity, looked at what it takes to become a real time enterprise and the role of change data capture in enabling the transformation. As organizations embrace AI and machine learning versus historical views of the past, they are also moving to real time computing and away from batch processing, said Potter. Traditional approaches included business reporting, batch analysis of data at rest, and use of transactional sources, but modern approaches also incorporate data science and advanced analytics, real-time processing of data in flight, and transactional data as well as new … 2018-05-24 00:00:00 Read the full story.  

Career paths in Business Analytics and Data Science World

“Data Scientist: The Sexiest Job of the 21st Century” is one of the most popular Harvard Business Review (HBR) articles and has inspired tons of people to pursue their careers in the field of analytics. One of the main themes of this article published in HBR was the trend of growing jobs in the analytics industry. The exact same inference was predicted by IBM recently saying that the number of US data professionals will increase from 364,000 to 2.72 million by 2020! 2018-05-28 08:59:24+05:30 Read the full story.  

How Andrew Ng Perceives The AI-Powered World

Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. … 2018-05-25 05:05:43+00:00 Read the full story.  

Using Machine Learning to Monitor Social Media Crises

Machine learning can be applied to sentiment analysis of unstructured data in the context of social media. As more and more people tap into social media tools to voice positive and negative opinions, it’s important to predict a social media “crisis” before one occurs. Jana Mitkovska, project manager, Raytion, and Christian Puzicha, senior solutions architect, Raytion GmbH, presented their session, “Self-Learning… 2018-05-23 00:00:00 Read the full story.  

David Weinberger Considers the Benefits and Risks of AI

Data Summit 2018 kicked off this week with a keynote by David Weinberger, senior researcher, Harvard’s Berkman Center for Internet & Society, titled “Once We Know Everything … or Suppose AI is Right?” Throughout history, it has been the goal of people to use tools to “anticipate and narrow” by taking information, and lessons learned from the past in order to control and prepare for the possibilities that may be encountered again so they can limit risk and increase the potential for success, said Weinberger. With the arrival of big data, machine learning, data interoperability, and all-to-all connections, machines are changing long-established concepts of what we know and what is able to be known. 2018-05-22 00:00:00 Read the full story.  

Anne Buff and Lynda Partner Explore What it Means to be a Data-Driven Enterprise

Two perspectives on becoming a data driven enterprise were discussed at Data Summit 2018 in presentations by Anne Buff, business solutions manager, SAS Best Practices, SAS Institute; and Lynda Partner, VP, Marketing & Analytics as a Service, Pythian. The demand to become a data-driven business with a competitive edge in the digital economy is greater now than ever. As we embrace the idea that the analytics economy will power the digital economy … 2018-05-22 00:00:00 Read the full story.  

Using Machine Learning to Report News

Machine learning is changing the way we interact with things and each other. As artificial intelligence gains steam, what we know about the world is changing. Reuters is introducing a new tool called News Tracer. It is a capability that applies AI in journalism to find events breaking on Twitter. It assigns them a newsworthiness score so people can focus on the events that are important. John Duprey, senior arc… 2018-05-22 00:00:00 Read the full story.  

This Media Startup Is Beating the Competition With a Newsroom Run by Robots

On Feb. 13 last year, the half-brother of North Korean dictator Kim Jong-Un was killed in an airport in Malaysia, in what the U.S. Department of State concluded was an assassination using a nerve agent. As North Korea and Malaysia were roiled in a diplomatic dispute, one entrepreneur in Japan and his budding news service were about to reap some attention. News of Kim Jong-Nam’s death was quickly picked up in Japan not by one of the country’s giant media conglomerates, but by a small startup. JX Press Corp., a news technology venture founded in 2008 by Katsuhiro Yoneshige while he was still a freshman in college, reported the incident more than half an hour faster than the big names, according to one observer. It did so even though it has no journalists, let alone any international bureaus. 2018-05-27 00:00:00 Read the full story.  

Qualcomm claims its on-device voice recognition is 95% accurate

At the Re-Work Deep Learning Summit in Boston, Chris Lott, an artificial intelligence researcher at Qualcomm, gave a glimpse into his team’s work on a new voice recognition program. The system, which works locally on a smartphone or other portable device, comprises two kinds of neural networks: a recurrent neural network (RNN), which uses its internal state, or memory, to process inputs, and a convolutional neural network, a neural network that … 2018-05-25 00:00:00 Read the full story.  

Using Asynchronous Method For Deep Reinforcement Learning

Machine Learning applications have propelled artificial intelligence to achieve realistic results to a great extent. This can be largely attributed to improved research and developments in areas like neural networks — particularly deep neural networks. The advancements in these networks have led to other areas of ML, like reinforcement learning (RL), to grow parallelly. 2018-05-25 10:28:46+00:00 Read the full story.  

AI marks the beginning of the Age of Thinking Machines

Every day brings considerable AI news, from breakthrough capabilities to dire warnings. A quick read of recent headlines shows both: an AI system that claims to predict dengue fever outbreaks up to three months in advance, and an opinion piece from Henry Kissinger that AI will end the Age of Enlightenment. Then there’s the father of AI who doesn’t believe there’s anything to worry about. Robert Downey, Jr. is in the midst of developing an eight-part documentary series about AI to air on Netflix. 2018-05-25 00:00:00 Read the full story.  

Microsoft is developing a tool to help engineers catch bias in algorithms

Microsoft is developing a tool that can detect bias in artificial intelligence algorithms with the goal of helping businesses use AI without running the risk of discriminating against certain people. Rich Caruana, a senior researcher on the bias-detection tool at Microsoft, described it as a “dashboard” that engineers can apply to trained AI models. “Things like transparency, intelligibility, and explanation are new enough to the field that few … 2018-05-25 00:00:00 Read the full story.  

Free Ebook Offers Insight on 16 Open Source AI Projects

Open source AI is flourishing, with companies developing and open sourcing new AI and machine learning tools at a rapid pace. To help you keep up with the changes and stay informed about the latest projects, The Linux Foundation has published a free ebook by Ibrahim Haddad examining popular open source AI projects, including Acumos AI, Apache Spark, Caffe, TensorFlow, and others. “It is increasingly common to see AI as open source projects,” Haddad said. And, “as with any technology where talent … 2018-05-22 12:33:32+00:00 Read the full story.  

Equity Market Innovation Leads to Venue Proliferation

Several startups are planning to launch either new venues or order types, and even listing standards, to solve problems in US equity trading. At last month’s SIFMA Equity Market Structure conference, executives with the following three startups and one established exchange discussed their particular market innovations and how each one fits into the existing equity market structure. Imperative Execution is on the brink of launching a dark pool t… 2018-05-25 16:25:37 Read the full story.  

Microsoft says AI is finally ready for broader use to help solve Earth’s environmental woes

REDMOND, Wash. — Lucas Joppa agrees we’re living in the Information Age. But he wishes that the present tech era wasn’t so navel gazingly focused on Homo sapiens. “I want an Information Age that encapsulates all information about life on Earth,” said Joppa, who is Microsoft’s first chief environmental scientist — and likely the first chief of this kind anywhere in the tech sector. “We’ve allowed ourselves to exist in a world where we’re completely flying blind to the rest of the life on Earth,” Joppa said. “We do that at our own peril, and it exhibits an exceptional lack of wonder about where we are and who we are and why we’re here.” 2018-05-23 22:16:19-07:00 Read the full story.  

GPU based servers can help solve Big Data energy woes

Big data keeps getting bigger, and companies may think they are reducing their carbon footprint by moving to the Cloud but this move could actually be making things worse for the environment. With each new data center created for big cloud providers, tens of thousands of new racks need to be installed, which translates into a further strain on energy resources. One way to reduce the carbon impact is … 2018-05-22 00:00:00 Read the full story.  

Strategies for Overcoming Big Data Integration Challenges at Data Summit 2018

With the rise of big data, there is the need to leverage a wider variety of data sources as quickly as possible for real-time decision making in mission-critical environments. Presentations at Data Summit 2018 showcased real-world scenarios where data integration is providing value. At Data Summit, Joseph deBuzna, VP, Field Engineering, HVR, showed how HVR helped a global financial services company that needed to architect a cloud-based trading data analytics platform. 2018-05-23 00:00:00 Read the full story.  

IBM’s head of Watson likes Elon Musk but ‘hates’ A.I. scaremongering

Warnings of artificial intelligence (AI) posing a threat to humanity are “not helpful,” a top executive at IBM has said. While critics like Tesla CEO Elon Musk have warned about the risks of developing AI, David Kenny, IBM’s senior vice president of Watson and Cloud, said the technology is already proving to be beneficial. “It’s making things safer in cybersecurity, it’s helping doctors and nurses and patients better find health … 2018-05-28 00:00:00 Read the full story.  

Pure Storage CEO says companies need to keep data ‘hot’ to work with AI

“The interest in A.I. by corporations is just off the charts,” Giancarlo said on CNBC’s “Squawk Alley.” “At Pure, we are able to…feed GPUs, high speed applications, and A.I. environments — at the speed they want that data to provide the intelligence companies want to make their businesses better.” Since A.I. is all about crunching huge amounts of data, older, tiered storage systems that rank data by age aren’t nimble enough to grant researchers quick access to even the oldest data sets, says Giancarlo. “These days, people want access to data, whether it was last week or last year or last decade. And in order to do that, the data needs to be kept hot,” Giancarlo said. “If you want to look at long term trends and data…you want to be able to mine it for more than a week or two.” 2018-05-22 00:00:00 Read the full story.  
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JupyterLab and Notebook News. 08, May 2018

News clips provided algorithmically.

Anaconda’s Damian Avila on the 2017 ACM Software System Award for Jupyter – Anaconda

I am very happy to inform you that Project Jupyter has been awarded the 2017 ACM Software System Award! As part of the Jupyter Steering Council, I am one of the official recipients of the award, but I wanted to highlight that I am just one member of a large group of people (contributors and users) working together to push the Project Jupyter forward and beyond its limits. Project Jupyter is an essential part of my life. It gave me the opportuni… 2018-05-02 13:21:08-05:00 Read the full story. CloudQuant Thoughts: Congrats to the team. We obviously believe that the ability to compute interactively and to research data will lead to smart opportunities for quantitative trading.

Linear Algebra for Deep Learning – Towards Data Science

Linear algebra is a form of continuous rather than discrete mathematics, many computer scientists have little experience with it. A good understanding of linear algebra is essential for understanding and working with many machine learning algorithms, especially deep learning algorithms. Deep Learning is a subdomain of machine learning, concerned with the algorithm which imitates the function and structure of the brain called the artificial neura… 2018-05-07 13:36:25.969000+00:00 Read the full story. CloudQuant Thoughts: Linear algebra (and finite mathematics) are skills that every data scientists should review.  
Below the Fold

50+ Useful Machine Learning & Prediction APIs, 2018 Edition

By Pedro Lopez, KDnuggets. An API is a set of routines, protocols and tools for building software applications. For KDnuggets’ third edition of this post, we removed discontinued APIs from the list of 2017, and updated it with new elements. All APIs are categorized into emerging application groups: Face and Image Recognition. Text Analysis, NLP, Sentiment Analysis. Language Translation. Machine Learning and prediction. Within each group of … 2018-05-05 00:00:00 Read the full story.  

Google’s Android development studio gets a new update with visual navigation editing

Android’s development studio is getting a new update as Google rolls out Android Studio 3.2 Canary, adding new tools for visual navigation editing and Jetpack. The new release includes build tools for the new Android App Bundle format, Snapshots, a new optimizer for smaller app code and a new way to measure an app’s impact on battery life. The Snapshots tool is baked into the Android Emulator and is geared toward getting the emulator up and runn… 2018-05-08 00:00:00 Read the full story.

Google’s ML Kit makes it easy to add AI smarts to iOS and Android apps

At its I/O developer conference, Google today introduced ML Kit, a new software development kit (SDK) for app developers on iOS and Android that allows them to integrate into their apps a number of pre-built Google-provided machine learning models. One nifty twist here is that these models — which support text recognition, face detection, barcode scanning, image labeling and landmark recognition — are available both online and offline, depending … 2018-05-08 00:00:00 Read the full story.

Hardware Advances Make 2018 the Year to Monetize Neural Nets

Neural networks and deep learning are frequently recognized as the key to futuristic innovations like facial recognition, with consumers paying up for the novelty of using their visage to unlock their phones, and police squads partnering with AI startups to catch criminals. But the use cases for neural nets extend well beyond the flashy, and innovations in hardware are enhancing accessibility, signaling it’s time for more companies to find approp… 2018-05-08 00:00:00 Read the full story.

Google launches ML Kit for Android and iOS developers

Google today announced the debut in beta of ML Kit, a software development kit optimized for deploying AI for mobile apps on app development platform Firebase. ML Kit, available for both Android and iOS developers, can call on APIs both on-device and in the cloud. AI in mobile apps can do a range of things, such as extract the nutrition information from a product label or add style transfers, masks, and other effects to a photo. The news was an… 2018-05-08 00:00:00 Read the full story.

Detecting Breast Cancer with Deep Learning – Towards Data Science

Invasive ductal carcinoma (IDC) also known as infiltrating ductal carcinoma is most common type of breast cancer. American Cancer Society estimates more than 180,000 women in the United States find out they have invasive breast cancer every year. Most of these cancers are diagnosed with IDC. Accurately identifying and categorizing breast cancer subtype is an important task. Automated methods based on AI can significantly save time and reduce err… 2018-04-27 15:19:45.259000+00:00 Read the full story.    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.

JupyterLab and Notebook News. 17, April 2018

JupyterCon 2018, NYC August 21–25 – Jupyter Blog

… concerns, new laws (GDPR), the evolution of computation, plus good storytelling and communication in general — as we’ll explore with practitioners throughout the conference. Recent beta release of JupyterLab embodies the meta-theme of extensible software architecture for interactive computing with data. While many people think of Jupyter as a “notebook,” that’s merely one building block needed for interactive computing with data. Other building blocks include terminals, file browsers, LaTeX, markdown, rich outputs, text editors, and renderers/viewers for different data formats. JupyterLab is the next… 2018-03-15 18:30:47.325000+00:00 https://blog.jupyter.org/jupytercon-2018-nyc-august-21-25-5571d7454d5b?source=collection_home—6——2—————- CloudQuant Thoughts: CloudQuant has been using the beta version of JupyterLab for our internal portfolio managers in their research for Alpha Signals. This platform is very useful in for the data science portion of algorithm development.

Reproducible Data Dependencies for Python [Guest Post]

… open source projects in the Jupyter ecosystem and the problems they attempt to solve. If you would like to submit a guest post to highlight a specific tool or project, please get in touch with us. Jupyter Notebooks go a long way towards making computations reproducible and sharable. Nevertheless, for many Jupyter users, it remains a challenge to manage datasets across machines, over time, and across collaborators — especially when those datasets are large or change often. Quilt Data is a company that supports Quilt, an open source project to version and package data. The Quilt team recently released an exte… 2018-03-13 17:01:01.088000+00:00 https://blog.jupyter.org/reproducible-data-dependencies-for-python-guest-post-d0f68293a99?source=collection_home—6——3—————- CloudQuant Thoughts: The data challenges don’t only include data formats, ingestion, and consumption. It also must cover the legal issues of who can access the data. Not all data is public!  

What’s New in Deep Learning Research: Inside Google’s Semantic Experiences

… deep neural network (DNN) to produce sentence embeddings. The Google research shows that the primary advantage of the DAN encoder is that compute time is linear in the length of the input sequence. TensorFlow Models The Google research team didn’t stop at the theoretical work and published an implementation of the Universal Sentence Encoder in the TensorFlow Hub. Using the encoder doesn’t require more than a handful of lines of code as shows in the following code snippet. 2018-04-16 12:54:19.781000+00:00 https://towardsdatascience.com/whats-new-in-deep-learning-research-inside-google-s-semantic-experiences-4536d57c685?source=collection_home—4——2—————-

Does Deep Learning Represent A New Paradigm In Software Development?

… to tune the model. Weidman gives an image classification example, wherein to train an image classifier, the developer loads in the images, and then uses an off-the-shelf code from a library like Keras to show the model structure. By searching how to perform “image classifier keras”, a developer can train a model with less than 20 lines of code. Given this scenario, Weidman argues for budding data scientists entering the field, knowing how a model works would not be mission critical. The more important thing would be ensuring data quality and building the right checks for the model, he adds. … 2018-04-16 04:45:49+00:00 https://analyticsindiamag.com/does-deep-learning-represent-a-new-paradigm-in-software-development/

Essentials of Deep Learning: Getting to know CapsuleNets (with Python codes)

…Neural Network Capsule Network Multi-layer Perceptron For our first attempt, let us build a very simple Multi-layer Perceptron (MLP) for our problem. Below is the code to build an MLP model in keras: 2018-04-12 08:37:47+05:30 https://www.analyticsvidhya.com/blog/2018/04/essentials-of-deep-learning-getting-to-know-capsulenets/

Google AI chief Jeff Dean discussing the applications of machine learning at the company’s TensorFlow Dev Summit


Comet.ML is the GitHub for Machine Learning Models

…our machine learning models, code, experiments and even hyperparameters You only need to add the tracking code to your preferred tool It supports all the popular tools and libraries like R, python, TensorFlow, Keras, among others Introduction GitHub has gained unparalleled popularity over the years for it’s amazing flexibility in allowing teams to collaborate and contribute to projects. Along the same lines comes Comet.ML, a tool that enables data scientists and machine learning practitioners to automatically track their machine learning code, experiments, hyperparameters, and model results. It add… 2018-04-06 11:04:59+05:30 https://www.analyticsvidhya.com/blog/2018/04/comet-ml-is-the-github-for-machine-learning-models/

5 Things You Need to Know about Big Data

…cripting Business and scientific applications of Big Data Big databases and NoSQL including MongoDB , Cassandra and Neo4J , and Analytics, machine learning and data visualisation using Weka , R and scikit-Learn , and Optimisation and heuristics for big problems Cluster computing with Hadoop, Spark, Hive and MapReduce Related:… 2018-03-05 00:00:00 http://www.kdnuggets.com/2018/03/5-things-big-data.html

Google Launches Machine Learning Course for the World

…a researcher, an entrepreneur, a professional, the course is for anyone and everyone. The Machine Learning course is a crash course provided by Google, which provides hands-on practice on TensorFlow APIs along with video lectures and various lessons. TensorFlow is a Machine Learning library provided by Google, which focuses on building machine learning products and tools. The course provided by Google consists of the following: 40+ Exercises 25 Lessons 15 Hours Lectures directly from Google Researchers Real World Case Studies Interactive Visualization of algorithms in action And much more … 2018-03-03 04:31:55+00:00 http://technoitworld.com/google-launches-machine-learning-course-world/

What Is TensorLayer & How Does It Differ From TensorFlow ML Libraries?

…sourcing most of their work. We explore one such open-source DL and RL software library called TensorLayer, which is a part of Google’s popular machine learning and numerical computational framework TensorFlow. The idea behind the new library was to facilitate a modular approach to DL as well as RL to tackle complexity and iterative tasks when it comes to large neural networks and their interactions. It was first released in 2016 and gradually adopted changes along the way to become the most sought after libraries for DL.The entire code for TensorLayer is written in Python – the most preferred programm… 2018-04-05 12:08:52+00:00 https://analyticsindiamag.com/what-is-tensorlayer-and-how-is-it-different-from-tensorflows-other-machine-learning-libraries/

Google Launches Machine Learning Course for the World

…ent, a researcher, an entrepreneur, a professional, the course is for anyone and everyone. The Machine Learning course is a crash course provided by Google, which provides hands-on practice on TensorFlow APIs along with video lectures and various lessons. TensorFlow is a Machine Learning library provided by Google, which focuses on building machine learning products and tools. The course provided by Google consists of the following: 40+ Exercises 25 Lessons 15 Hours Lectures directly from Google Researchers Real World Case Studies Interactive Visualization of algorithms in action And much more … 2018-03-03 04:31:55+00:00 http://technoitworld.com/google-launches-machine-learning-course-world/#respond

How Alibaba Used Reinforcement Learning To Change Real-Time Bidding

…ns, it is very hard to find the accurate data. The upperhand of the use of simulator is the auctions with unique bids which can be simulated with the help of the entire auction database. Distributed TensorFlow Cluster: The RL model is trained on the tensorflow cluster in a varied manner with the servers to facilitate the handling of the weights in the layers. The model was ran on a number of CPUs and GPUs to parallely input the billions of samples since agents have to be trained simultaneously. Search Auction Engine: The auction engine is the master component. It sends requests and impression-level … 2018-04-04 11:52:41+00:00 https://analyticsindiamag.com/how-this-research-by-alibaba-group-has-used-reinforcement-learning-to-change-real-time-bidding/

Introduction to KNN, K-Nearest Neighbors : Simplified

…When do we use KNN algorithm? How does the KNN algorithm work? How do we choose the factor K? Breaking it Down – Pseudo Code of KNN Implementation in Python from scratch Comparing our model with scikit-learn When do we use KNN algorithm? KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects: 1. Ease to interpret output 2. Calculation time 3. Predictive Power Let us take a few examples to place KNN in the scale : KNN algorithm fairs… 2018-03-26 03:18:09+05:30 https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/

Sponsored Content: Training Machine Learning Models with MongoDB

…duplicate URLs, and their associated text data, were not added to the database. Next, the entire dataset needed to be parsed using NLP and passed in as training data for the TFIDF Vectorizer (in the scikit-learn toolkit) and the Latent Dirichlet Allocation (LDA) model. Since both TFIDF and LDA require training on the entire dataset (represented by a matrix of ~70k rows x ~250k columns), I needed to store a lot of information in memory. LDA requires training on non-reduced data in order to identify correlations between all features in their original space. Scikit Learn’s implementations of TFIDF and LDA a… 2018-04-02 00:00:00 http://www.dbta.com/Editorial/Actions/Sponsored-Content-Training-Machine-Learning-Models-with-MongoDB-123586.aspx  
Trevor Trinkino Quantitative Trader

FXCM Machine Learning with Trevor Trinkino

On February 8th Trevor Trinkino presented Machine Learning with FXCM in a webinar. During this presentation, he promised to make available his machine learning Python Notebook and the supporting data file. These are available on our Google drive at: