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All Blog Posts Tagged 'dsc_graph' (65)

Differential ML on TensorFlow and Colab

Brian Huge and I just posted a working paper following six months of…

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Added by Antoine Savine on May 25, 2020 at 11:30am — No Comments

Milvus: A big leap to scalable AI search engine

The challenge with data search

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Added by Kate Shao on May 22, 2020 at 11:00pm — No Comments

What are “Recurrent Neural Networks”, and how it is different than “Convolutional Neural Networks”

CNN and RNN are amongst most important algorithm of Neural Network family, also they differ in their network process and solving problems.

So talking about their differences:

CNN are used to solve classification and regression problems and RNN are used to solve sequence information.

CNN are used for 2D image…

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Added by Sameer Nigam on May 21, 2020 at 12:00pm — No Comments

Random Forest Classification explained in detail and developed in R

In supervised machine learning algorithms, Random Forest stands apart as it is arguably the most powerful classification model. When Microsoft developed their X-box game which enables you to play as per the movement of your posture, they used Random Forest over any other machine learning algorithm and over ANN (Advanced Neural Networks) as well !

I am assuming that if you are reading this blog, you are well versed with Decision Tree Classification. However, if you are not, need not…

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Added by Sourav Kumar Das on May 10, 2020 at 1:30am — No Comments

ELAINE use case – Improving statistical prediction on financial market with Symbolic Logic

In my previous post, I introduced the ELAINE Community Tool that can be used to discover variables from textual communications.

 

Statistical predictors work well for charting the course of economic activities based on macro factors that elicit supply and demand if the market environment is within a close proximity of a pattern that resembles previous economic cycles. Unfortunately, in today’s geo-political environment, many of these new variables are injecting forces that…

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Added by Sing Koo on March 9, 2020 at 4:30am — 2 Comments

Finding organic clusters in complex data-networks

This article was written by Graph Commons.

A common task for a data scientist is to identify clusters in a given data set. The idea is to simply find groups of objects that have more connections or similarities to one another than they do to outsiders. In the study of networks, we use clustering to recognize…

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Added by Andrea Manero-Bastin on March 9, 2020 at 3:30am — No Comments

Google Releases TensorFlow Quantum

Today, Google published the following paper:

TensorFlow Quantum: A Software Framework for Quantum Machine Learning, 

TensorFlow Quantum is software for doing Quantum Bayesian Networks. QB nets have been a dream of mine for 24 years, although Google's paper, despite having 20 authors and 129 references, never cites any of my work. When I first had the idea of Quantum Bayesian Networks, I thought it was such a cool idea that, within a span of a year, I published…

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Added by Robert R. Tucci on March 8, 2020 at 10:30pm — 1 Comment

How you can explain Machine Learning models ?

Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most of the cases, users do not understand how these models make predictions. The lack of understanding makes it difficult for policy makers to justify their decisions. Most of the ML models are black boxes that do not explain on its own why it reached a specific recommendation or a decision. This forces the users to say…

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Added by Janardhanan PS on February 27, 2020 at 7:00pm — No Comments

Ability to generalize - A measure of intelligence ?

Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model's ability to make accurate predictions from never before seen data. Well generalized models possess intelligence to work on data from new scenarios. This is true for human intelligence also. Children start learning from examples and initially they fail to respond properly to unforeseen situations. Gradually they acquire generalization skills to respond to all… Continue

Added by Janardhanan PS on February 16, 2020 at 9:02pm — No Comments

The 10 Deep Learning Methods AI Practitioners Need to Apply

This article was written by James Le.

Neural networks are one type of model for machine learning; they have been around for at least 50 years. The fundamental unit of a neural network is a node, which is loosely based on the biological neuron in the mammalian brain. The connections between neurons are also modeled on…

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Added by Andrea Manero-Bastin on February 9, 2020 at 12:00pm — No Comments

Machine Learning Mindmap

Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more startups are relying mainly on ML. 

At the same time, machine…

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Added by Igor Bobriakov on January 28, 2020 at 8:12am — 1 Comment

Decision Tree Fundamentals

Presentation on decision tree fundamentals such as  finding best split, gini, entropy, misclassification error, gain ratio, numerical examples.…

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Added by Siddhaling Urolagin on January 23, 2020 at 9:30pm — 1 Comment

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

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Added by Janardhanan PS on January 20, 2020 at 11:30pm — 1 Comment

Deploying machine learning models using Agile

In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules.

 

How this relates to Agile?

 

Deployment of AI comes within the scope the normal SDLC (software development lifecycle)

So, normal Agile techniques like scrum, sprints, backlog…

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Added by ajit jaokar on January 20, 2020 at 1:06pm — No Comments

Importance of Hyper-parameters in Model development

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…

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Added by Janardhanan PS on January 16, 2020 at 12:00am — 1 Comment

Key Graph Based Shortest Path Algorithms With Illustrations - Part 1: Dijkstra's And Bellman-Ford Algorithms

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details.  A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases.  There are several graph based algorithms and most notable are the shortest path…

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Added by Murali Kashaboina on January 14, 2020 at 3:00pm — No Comments

Connections between Neural Networks and Pure Mathematics

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are examples of AI-based applications that affect us on a daily basis.

Deep learning has been systematically improving the state of the art in areas such as speech recognition, autonomous driving, machine translation,…

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Added by Marco Tavora on January 5, 2020 at 5:30am — No Comments

Neural Quantum States



Picture by By Tatiana Shepeleva/shutterstock.com

One of the most challenging problems in modern theoretical physics is the so-called many-body problem. Typical many-body systems are composed of a large number of strongly interacting particles. Few such systems are amenable to exact…

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Added by Marco Tavora on January 5, 2020 at 4:30am — No Comments

Setting the Cutoff Criterion for Probabilistic Models

For decision making, human perception tends to arrange probabilities into above 50% and below - which is plausible. For most probabilistic models in contrast, this is not the case at all. Frequently, resulting probabilities are neither normal distributed between zero and one with a mean of 0.5 nor correct in terms of absolute values. This is not seldom an issue accompanied with the existence of a minority class - in the underlying dataset.

For example, if the result of a…

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Added by Frank Raulf on January 4, 2020 at 3:00am — No Comments

Naive Bayes Classifier using Kernel Density Estimation (with example)

Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities of an event:

p(d)   (1)

By observing new data x, the statistician will adjust his opinions to get the "a posteriori" probabilities.

p(d|x)   (2)

The conditional probability of an event d given x is the share of  the joint…

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Added by Frank Raulf on January 3, 2020 at 4:30am — No Comments

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