Deep Learning Cheat Sheet (using Python Libraries)

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. Originally posted here in PDF format. Click on the image below to zoom in.


Re-thinking Enterprise business processes using Augmented Intelligence

In the 1990s, there was a popular book called Re-engineering the Corporation. Looking back now, Re-engineering certainly has had a mixed success – but it did have an impact over the last two decades. ERP deployments led by SAP and others were a direct result of the Business Process re-engineering phenomenon. So, now, with the rise of AI: Could we think of a new form of Re-engineering the Corporation – using Artificial Intelligence? The current group of Robotic process automation companies focus on the UI layer. We could extend this far deeper into the Enterprise. Leaving aside the discussion of the impact of AI on jobs, this could lead to augmented intelligence at the process level for employees (and hence an opportunity for people to transition their careers in the age of AI). Here are some initial thoughts. I am exploring these ideas in more detail. This work is also a part of an AI lab we are launching in London and Berlin in partnership with UPM and Nvidia both for Enterprises and Cities


Emotion Detection Using Machine Learning

Pulling out context from the text is one of the most remarkable procurements obtained using NLP. A few years back, context extraction was to detect the sentiment from the text and then the definition took a step forward towards emotion detection. These two are very different terms. The sentiment can be positive, negative, neutral while emotions are more refined categories among these three. A positive sentiment could be attributed to happy, excited and even a funny emotion. Similarly, anger, disgust, and sad emotions make the sentiment negative.


5 Digital Intelligence Rules in the Age of Insights

• Insights for Digital Disruption
• Time Series Analysis, Not Snapshots
• Smart Digital Intelligence
• A Single Source of Truth
• Big Data is Only the Beginning


Hadoop is Not Failing, it is the Future of Data – Why Hadoop Is Thriving and Will Continue to do so…

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging visualization but also to personalize services customers care about – while working across multiple channels/modes of interaction. Mobile applications first began forcing the need for enterprise applications to support multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. Healthcare is a close second where caregivers expect patient, medication & disease data at their fingertips with a few finger swipes on an iPad app.Big Data technology evolved to overcome the limitations of existing data approaches (RDBMS & EDW) to keep up data architecture & analysis challenges inherent in the Digital application stack.


Data Science for Operational Excellence (Part-4)

Suppose your friend is a restaurant chain owner (only 3 units) facing some competitors challenges related to low price, lets call it a price war. Inside his business he knows that there’s no much cost to be cut. But, he thinks that, maybe if he tries harder to find better supplier with low freight and product costs, he could be in a better situation. So, he decided to hire you a recent grad data scientist to figure out how to solve this problem and to build a tool to make your findings to be incorporated in his daily operations. As a Data Scientist you know that this problem could be solved through the use of lpSolve package. Our goal here is to expand your knowledge to create custom constraints to be used in real business problems.


The Guerrilla Guide to Machine Learning with Python

Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.
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