TensorFlow is Google’s open source machine learning and neural network library. CloudQuant crowd researchers and data scientists use TensorFlow in conjunction with Jupyter notebooks as part of their quantitative research.



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.    
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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  
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 7 2017

August 7, 2017

Citadel’s Flagship Funds Gain Almost 7% This Year

…Citadel’s Tactical Trading Fund, which uses equity and quantitative strategies, rose 3 percent last month, bringing year-to-date performance through July to 4.9 percent. …  

Hedge funds lose more than half a billion on wrong-way bet against Tesla

It’s not just hedge funds that bet the wrong way on Tesla. Wall Street analysts, normally a very bullish crowd, were largely negative on the stock heading into the earnings report. They reiterated their bearishness in reports on Thursday, despite the stock pop. “We were surprised by the after hours move in TSLA shares and continue to be cautious on the stock, especially as the risk profile shifts from the hype of the Model 3 to execution, or ‘production hell’ as Elon Musk refers to it,” Cowen analyst Jeffrey Osborne wrote in a note. CloudQuant note — We wonder what the Algos were choosing? Were they different than the analysts?  

Watson Machine Learning is now Generally Available

IBM announced the general availability of the IBM Watson Machine Learning service. Over the past 12 months feedback from hundreds of users of the Watson Machine Learning (WML) service led to this announcement. CloudQuant note — We love seeing more people able to advance the cause of Data Science and Machine Learning  

Google chief funds new machine-learning effort at Princeton’s IAS

A $2 million donation will launch new research at the Institute for Advanced Study (IAS) in Princeton to forge an understanding of how machine learning evolves. Machine learning — sometimes called the leading edge of artificial intelligence — is the rapidly developing computer technology behind self-driving cars, complex web searches, medical and science applications, and face and speech recognition. Machine-learning programs synthesize knowledge in a way that’s analogous to how children learn. The programs take examples, generalize, and then develop rules and understanding about the world without being taught directly. With time, the programs become better at particular tasks. CloudQuant note — We love seeing academic chances to advance the cause of Data Science and Machine Learning  

10 hot data analytics trends — and 5 going cold

Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.
CloudQuant note — We are definitely in agreement on the topics of Scikit-learn, TensorFlow, and Jupyter Notebooks