Parsing Structured Documents with Custom Entity Extraction

There are lots of great tutorials on the web that explain how to classify chunks of text with Machine Learning. But what if, rather than just categorize text, you want to categorize individual words.

The Almighty Policy Gradient in Reinforcement Learning

A simple step by step explanation to the concept of policy gradients and how they fit into reinforcement learning. Maybe too simple.

Achieving Artificial General Intelligence (AGI) using Self Models

Moravec’s paradox is the observation made by many AI researchers that high-level reasoning requires less computation than low-level unconscious cognition. This is an empirical observation that goes against the notion that greater computational capability leads to more intelligent systems. However, we have today computer systems that have super-human symbolic reasoning capabilities. Nobody is going to argue that a man with an abacus, a chess grandmaster or a champion Jeopardy player has any chance at besting a computer. Artificial symbolic reasoning is technology that has been available for decades now and this capability is without argument superior in capability than what any human can provide. Despite this, nobody will claim that computers are conscious. Today, with the discovery of deep learning (i.e. intuition or unconscious reasoning machines), low-level unconscious cognition is within humanity’s grasp. In this article, I will explore the ramifications of a scenario where machine subjectivity or self-awareness is discovered prior to the discovery of intelligent machines. This is a scenario where self-awareness is not a higher reasoning capability. Let us ask, what if self-aware machines were discovered before intelligent machines. What would the progression of breakthroughs look like? What is the order of the milestones?

Integrating Machine Learning Models within Matured Business Process

Machine Learning today is reaching every business process of enterprise, helping to create value, enhance customer experience or to bring in operational efficiency. Business today have necessary infrastructure, right tooling and data to generate insights faster than before. While Machine Leaning models can have a significant and positive impact on how business process are run, it can also turn out to be risky, if put in live production without monitoring these models for reasonable amount of time. Major hurdle one hits is when organizations have some form of business rules embedded into their critical business process. These rules might have evolved over time taking real world domain knowledge into play and also might be performing exceptionally well. In these scenarios stakeholders typically might push-back on completely doing away with existing rules ecosystem. Challenge with rule based system is data and business scenario change faster today to a point where either rules are unable to catch up with real world scenario or it is very time consuming to create and maintain additional rules With the set background, this article is about how we can make use of best of both worlds (Rules + Machine Learning) and also over time measure performance of machine learning models with real world data to see if they can exist by themselves.

Should You Be Recommending Deep Learning Solutions in Your Company?

If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.

Automated data report storytelling in R

In this article, you learn how to make Automated data report storytelling in R for Credit Modelling. First you need to install the rmarkdown rmarkdown package into your R library. Assuming that you installed the rmarkdown rmarkdown , next you create a new rmarkdown rmarkdown script in R.

Customer Support Chatbots: Easier & More Effective Than You Think

Learn how to create your own free chatbot environment with just a few commands, as well as learning more about the benefits of customer service chatbots.

An Introductory Guide to Computer Vision

The fantasy that a machine is capable of simulating the human visual system is old. We’ve come a long way since the first university papers appeared back in the 1960s, as evidenced by the advent of modern systems trivially integrated into mobile applications. Today, computer vision is one of the hottest subfields of artificial intelligence and machine learning, given its wide variety of applications and tremendous potential. Its goal: to replicate the powerful capacities of human vision. But, what exactly is computer vision? How is it currently applied in different industries? What are some well-known business use cases? What tasks are typical to computer vision? In this guide, you’ll learn about the basic concept of computer vision and how it’s used in the real world. It’s a simple examination of a complex problem for anybody who has ever heard of computer vision but isn’t quite sure what it’s all about and how it’s applied.

R Studio Shortcuts and Tips – part 2

Welcome to the second part of R Studio shortcuts and tips! If you have not yet read r studio shortcuts and tips – part one, I strongly recommend to do it before proceeding further.

Part 2: Simple EDA in R with inspectdf

Previously, I wrote a blog post showing a number of R packages and functions which you could use to quickly explore your data set. Since posting that, I’ve become aware of another exciting EDA package: inspectdf by Alastair Rushworth! As is very often the case, I became aware of this package in a twitter post by none other than Mara Averick.

Understanding the 3 most common loss functions for Machine Learning Regression

A loss function in Machine Learning is a measure of how accurately your ML model is able to predict the expected outcome i.e the ground truth. The loss function will take two items as input: the output value of our model and the ground truth expected value. The output of the loss function is called the loss which is a measure of how well our model did at predicting the outcome. A high value for the loss means our model performed very poorly. A low value for the loss means our model performed very well. Selection of the proper loss function is critical for training an accurate model. Certain loss functions will have certain properties and help your model learn in a specific way. Some may put more weight on outliers, others on the majority. In this article we’re going to take a look at the 3 most common loss functions for Machine Learning Regression. I’ll explain how they work, their pros and cons, and how they can be most effectively applied when training regression models.

Reinforcement learning basics: stationary and non-stationary multi-armed bandit problem

The multi-armed (also called k-armed) bandit is an introductory reinforcement learning problem in which an agent has to make n choices among k different options. Each option delivers a (possibly) different reward from an unknown distribution which usually doesn’t change over time (i.e. it is stationary). If the distribution changes over time (i.e. it is not stationary), the problem gets harder because previous observations (i.e. previous games) are of little usefulness. In either case, the goal is to maximize the total reward obtained. This article reviews one (of many) simple solution for both a stationary and a non-stationary 5-armed bandit across 1000 games. Note that only some remarks of the full code will be showcased here, for the fully functional notebook, please see this github repository.

Detection Free Human Instance Segmentation using Pose2Seg and PyTorch

In recent years, research related to ‘humans’ in the computer vision community has become increasingly active because of the high demand for real-life applications, among them is instance segmentation. The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN perform them jointly. However, as human associated tasks becoming more common like human recognition, tracking etc. one might wonder why does the uniqueness of the ‘human’ category does not taken into account. The uniqueness of the ‘human’ category, can be well defined by the pose skeleton. Moreover, the human pose skeleton can be used to better distinguish instances with heavy occlusion than using bounding-boxes. In this post, I am going to review ‘Pose2Seg: Detection Free Human Instance Segmentation’, which presents a new pose-based instance segmentation framework for humans which separates instances based on human pose.

Machine Learning in Python NumPy: Neural Network in 9 Steps

1. Initialization
2. Data Generation
3. Train-test Splitting
4. Data Standardization
5. Neural Net Construction
6. Forward Propagation
7. Back-propagation
8. Iterative Optimization
9. Testing
This is how you can build a neural net from scratch using NumPy in 9 steps. Some of you might have already built neural nets using some high-level frameworks such as TensorFlow, PyTorch, or Keras. However, building a neural net using only low-level libraries enable us to truly understand the mathematics behind the mystery.

Deep Learning for Clinical Diagnostics

This is the fourth article in the series Deep Learning for Life Sciences. In the previous posts, I showed how to use Deep Learning on Ancient DNA, Deep Learning for Single Cell Biology and Deep Learning for Data Integration. Now we are going to dive into Biomedicine and learn why and how we should use Bayesian Deep Learning for patient safety.

Can we generate Automatic Cricket Commentary using Neural Networks ?

Like everything else, the world of cricket has also gone through a lot of technological transformations in the recent years. They way cricket is played and and how it is viewed all around the world have both changed as a result. In this post we discuss if neural networks are capable of generating cricket commentary by just watching it. There has been some work in the literature (can be found here, here and here) but they do not use neural networks. Being a believer in end to end deep learning, I think neural networks will seal the deal on this task in the near future. This is a hard problem to tackle, because apart from visual feature extraction, it involves very complex temporal dynamics and handling of long term dependencies. This is because commentary is generally highly contextualized by the development of current game, its significance in broader perspective (friendly match vs tournament), and histories of teams and players involved. Decontextualized explanation of what is happening appears to be a easier problem to solve and I can think of an architecture that can used for modelling this.