Artificial Intelligence, Machine Learning and Big Data – A Comprehensive Report
Artificial Intelligence and Machine Learning are the hottest jobs in the industry right now. 2018 has seen an even bigger leap in interest in these fields and it is expected to grow exponentially in the next five years! For instance, did you know that more than 50,000 positions related to Data and Analytics are currently vacant in India? We are excited to release a comprehensive report together with Great Learning on how AI, ML and Big Data are changing and evolving the world around us. Additionally, this report aims to provide an overview of the kind of career opportunities available in these fields right now, and the different roles we might see in the future.
Shannon versus semantic information processing in the brain
Claude Shannon insisted that attributing an interpretation or meaning to information will destroy its generality and limit its scope. According to him it is only the statistical nature of information that matters. Semantic information is on the other hand, meaning of the information. Pattern recognition plays a big role in neural signal processing. Patterns can be thought of encoded semantic information in neural signals. In neuroscience both types of information have been studied extensively, without ever mentioning the term ‘semantic information,’ whereas ‘Shannon information’ has become a household name among the neuroscientists, often even without the term ‘Shannon.’ In fact, neural information in general is a combination of both. In this review we highlight Shannon information theoretical aspects and semantic information theoretical aspects in neural information processing. In fact, neural information in general, is a combination of both. It has been elaborated how an organized study of semantic processing of neural information, particularly from a time series data mining point of view, can aid our understanding of information processing in the brain.
Big data analytics: Machine learning and Bayesian learning perspectives – What is done? What is not?
Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. An ignorance of observing the progression of this fast-growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic – transformative knowledge discovery – that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics.
Analytics Translator – The Most Important New Role in Analytics
The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.
MATLAB toolbox on regime switching copula estimation and simulation
You may want to check out my code on regime switching copula models.The toolbox is designed to estimate the parameters of a regime switching copula model, assuming two regimes. Each regime can be described by any of the following five copulas: …
Investigating Tensors with PyTorch
In this tutorial, you’ll learn about Tensors, PyTorch, and how to create a simple neural network with PyTorch.
Lists: N-Sized Chunks
In this tutorial, you shall work with lists and learn an efficient way to divide arbitrarily sized lists into chunks of a given size.
Deduce the Number of Layers and Neurons for ANN
There is an optimal number of hidden layers and neurons for an artificial neural network (ANN). This tutorial discusses a simple approach for determining the optimal numbers for layers and neurons for ANN’s.
(A brief) history of randomness, and simulation techniques
Hearing ‘there is a 10% chance of rain today’ or ‘the medical test has a positive predictive value of 75%’ shows that the probabilities are now everywhere. A probability is a quantity that is difficult to grasp, but essential when trying to theorize and measure chance, or randomness. And if mathematical theory finally came very late, as Hacking (2006) points out, this did not prevent insurance from developing early enough, and from having the first (actuarial) mortality tables even before the ‘probability of death’ or ‘life expectancy’ had a mathematical basis. And in the same way, many techniques were invented to ‘generate randomness’, before the explosion of the so-called Monte Carlo methods, in parallel with the development of computing (and the fact that a machine could generate chance).
The What-If Tool: Code-Free Probing of Machine Learning Models
Building effective machine learning (ML) systems means asking a lot of questions. It’s not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better: How would changes to a datapoint affect my model´s prediction? Does it perform differently for various groups-for example, historically marginalized people? How diverse is the dataset I am testing my model on? Answering these kinds of questions isn´t easy. Probing ‘what if’ scenarios often means writing custom, one-off code to analyze a specific model. Not only is this process inefficient, it makes it hard for non-programmers to participate in the process of shaping and improving ML models. One focus of the Google AI PAIR initiative is making it easier for a broad set of people to examine, evaluate, and debug ML systems. Today, we are launching the What-If Tool, a new feature of the open-source TensorBoard web application, which let users analyze an ML model without writing code. Given pointers to a TensorFlow model and a dataset, the What-If Tool offers an interactive visual interface for exploring model results.
First Wave of Spiking Neural Network Hardware Hits
Over the last several years we have seen many new hardware architectures emerge for deep learning training but this year, inference will have its turn in the spotlight. For those chips that can manage to do both sides of the workload on the same device with edge-ready efficiency, a market awaits-albeit an emerging one. Most devices geared toward convolutional, recurrent, and other large neural networks need relatively big amounts of on-chip memory for storing weights and layers for the long iterative training process. With the datacenter chips that do both training and inference, the same big piece of silicon does the much lighter lifting of inference, which is convenient but not at all efficient. These shortcomings of memory-laden AI chip architectures have opened the door for neuromorphic devices as well as other approaches to machine learning, including spiking neural networks. The problem is, both neuromorphic devices and spiking nets are far more oriented in research than commercial reality, even if the business case is emerging.
Machine Learning Cheat Sheets
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Machine Learning for Text Classification Using SpaCy in Python
spaCy is a popular and easy-to-use natural language processing library in Python. It provides current state-of-the-art accuracy and speed levels, and has an active open source community. However, since SpaCy is a relative new NLP library, and it´s not as widely adopted as NLTK. There is not yet sufficient tutorials available. In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.
The real story on container, cloud, and data adoption
Poll results reveal where and why organizations choose to use containers, cloud platforms, and data pipelines.
Progress for big data in Kubernetes
Kubernetes is really cool because managing services as flocks of little containers is a really cool way to make computing happen. We can get away from the idea that the computer will run the program and get into the idea that a service happens because a lot of little computing just happens. This idea is crucial to making reliable services that don´t require a ton of heroism to stand up or keep running. But there is a dark side here. Containers want to be agile because that is the point of containers in the first place. We want containers because we want to make computing more like a gas made up of indistinguishable atoms instead of like a few billiard balls with colors and numbers on their sides. Stopping or restarting containers should be cheap so we can push flocks of containers around easily and upgrade processes incrementally. If ever a container becomes heavy enough that we start thinking about that specific container, the whole metaphor kind of dissolves. STRATA DATA CONFERENCE Strata Data Conference in New York, September 11-13, 2018 Check it out So that metaphor depends on containers being lightweight. Or, at least, they have to be lightweight compared to the job they are doing. That doesn´t work out well if you have a lot of state in a few containers. The problem is that data lasts a long time and takes a long time to move. The life cycle of data is very different than the life cycle of applications. Upgrading an application is a common occurrence, but data has to live across multiple such upgrades.
Binary, beta, beta-binomial
I´ve been working on updates for the simstudy package. In the past few weeks, a couple of folks independently reached out to me about generating correlated binary data. One user was not impressed by the copula algorithm that is already implemented. I´ve added an option to use an algorithm developed by Emrich and Piedmonte in 1991, and will be incorporating that option soon in the functions genCorGen and addCorGen. I´ll write about that change some point soon.
Going from a human readable Excel file to a machine-readable csv with tidyxl
I won´t write a very long introduction; we all know that Excel is ubiquitous in business, and that it has a lot of very nice features, especially for business practitioners that do not know any programming. However, when people use Excel for purposes it was not designed for, it can be a hassle. Often, people use Excel as a reporting tool, which it is not; they create very elaborated and complicated spreadsheets that are human readable, but impossible to import within any other tool.
Transparent Reasoning: How MIT Builds Neural Networks that can Explain Themselves
Just yesterday, I was reviewing a neural network architecture built by one of the teams at Invector Labs. During the tests, I was very impressed with a couple of decisions made by the mode that I couldn’t quite explain myself. ‘That’s very clever’, I asked the team, ‘how does the model arrives to that decision?’. Before finishing the question I already knew the answer I was going to hear: ‘we don’t quite know’. Interpretability is one of the biggest challenges in deep learning solutions in the real world. Any basic deep learning model can contain dozens of hidden layers and millions of neurons interacting with each other. Additionally, the structure of the networks can change as it builds up new knowledge. Tracing at explaining the specifics of how a deep neural network arrives to a specific decision has proven to be almost impossible in many scenarios. Recently, researchers from the MIT Lincoln Laboratory’s Intelligence and Decision Technologies Group published a paper in which they proposed a method that can perform complex, human-like reasoning about images in an interpretable manner.
Forecasting Fundamentals You Should Know Before Building Predictive Models
To forecast, or not to forecast, that is the question. The history of human civilization is entwined with the history of methods we have tried with forecasting. While our ancestors observed the sky to forecast the weather, Data Scientists develop and train machine learning models to predict sales, risks, events, trends, etc. Accurate forecasting is a critical organizational capability, and businesses that do it well would have a big advantage to survive.
Feature selection — Correlation and P-value
Often when we get a dataset, we might find a plethora of features in the dataset. All of the features we find in the dataset might not be useful in building a machine learning model to make the necessary prediction. Using some of the features might even make the predictions worse. So, feature selection plays a huge role in building a machine learning model. In this article we will explore two measures that we can use on the data to select the right features.
Training Deep Neural Networks
Deep neural networks are key break through in the field of computer vision and speech recognition. For the past decade, deep networks have enabled the machines to recognize images, speech and even play games at accuracy nigh impossible for humans. To achieve high level of accuracy, huge amount of data and henceforth computing power is needed to train these networks. However, despite the computational complexity involved, we can follow certain guidelines to reduce the time for training and improve model accuracy. In this article we will look through few of these techniques.
The magic of LSTM neural networks
LSTM Neural Networks, which stand for Long Short-Term Memory, are a particular type of recurrent neural networks that got lot of attention recently within the machine learning community. In a simple way, LSTM networks have some internal contextual state cells that act as long-term or short-term memory cells. The output of the LSTM network is modulated by the state of these cells. This is a very important property when we need the prediction of the neural network to depend on the historical context of inputs, rather than only on the very last input.
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