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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

 

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