Added by Antoine Savine on May 25, 2020 at 11:30am — No Comments
The explosion in unstructured data, such as images, videos, sound records, and text,…Continue
Added by Kate Shao on May 22, 2020 at 11:00pm — No Comments
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…Continue
Added by Sameer Nigam on May 21, 2020 at 12:00pm — No Comments
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…Continue
Added by Sourav Kumar Das on May 10, 2020 at 1:30am — No Comments
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…Continue
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…Continue
Added by Andrea Manero-Bastin on March 9, 2020 at 3:30am — No Comments
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…Continue
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…Continue
Added by Janardhanan PS on February 27, 2020 at 7:00pm — No Comments
Added by Janardhanan PS on February 16, 2020 at 9:02pm — No Comments
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…Continue
Added by Andrea Manero-Bastin on February 9, 2020 at 12:00pm — No Comments
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…Continue
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…Continue
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…Continue
Added by ajit jaokar on January 20, 2020 at 1:06pm — No Comments
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…Continue
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…Continue
Added by Murali Kashaboina on January 14, 2020 at 3:00pm — No Comments
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,…Continue
Added by Marco Tavora on January 5, 2020 at 5:30am — No Comments
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…Continue
Added by Marco Tavora on January 5, 2020 at 4:30am — No Comments
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…Continue
Added by Frank Raulf on January 4, 2020 at 3:00am — No Comments
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:
By observing new data x, the statistician will adjust his opinions to get the "a posteriori" probabilities.
The conditional probability of an event d given x is the share of the joint…Continue
Added by Frank Raulf on January 3, 2020 at 4:30am — No Comments