Artificial Intelligence (AI) Solutions, particularly those based on Deep Learning in the areas of Computer Vision, are done in a cloud-based environment requiring heavy computing capacity.
Inference is a relatively lower compute-intensive task than training, where latency is of greater importance for providing real-time results on a model. Most inference is still performed in the cloud or on a server, but as the diversity of AI applications grows, the centralized training and inference paradigm is coming into question.
It is possible, and becoming easier, to run AI and Machine Learning with analytics at the Edge today, depending on the size and scale of the Edge site and the particular system being used. While Edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today’s x86 commodity servers. …
According to research, 85% of the companies believe that Website Conversion Rate Optimization’s impact on their digital marketing goals is significantly higher than other marketing efforts.
Achieving the traffic goals for your website and other digital resources via paid advertisement & search, an effective SEO and social media campaigns, is a great stepping stone towards realizing the digital marketing goals.
But, majority of the people visiting your site may not be completing that ONE specific action which you intend them to take.
Conversion and Conversion Goals:
The ultimate conversion goal, does not necessarily have to be a purchase, it could be an email signup or an app download or filling up a survey, this. It is a hard-learned fact, that traffic-only strategies, if not backed by Conversion Plan, drain the investments in digital resources. It is one of the most critical activities, to bridge this gap between traffic and conversions. …
Among the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”. In this blog post, we shall continue our discussion further on “Feature Selection in Machine Learning”. The topics for this post are Variable Ranking or Feature Ranking, and Feature Subset Selection Methods.
In the previous blog post, I’d introduced the the basic definitions, terminologies and the motivation in Feature Selection. For your quick reference, the link to the preceding blog post of the Series, below:
Variable Ranking is the process of ordering the features by the value of some scoring function, which usually measures feature-relevance.
Resulting set: The score S(fi) is computed from the training data, measuring some criteria of feature fi. By convention a high score is indicative for a valuable (relevant) feature. …
Companies have more data than ever, so it’s crucial to ensure that your analytics team is uncovering actionable, rather than interesting data — knowing the difference between Interesting Data and Useful Data. Amongst the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”.
An universal problem of intelligent (learning) agents is where to focus their attention. It is very critical to understand “What are the aspects of the problem at hand are important/necessary to solve it?” i.e. discriminate between the relevant and irrelevant parts of experience.
Problem of selecting some subset of a learning algorithm’s input variables upon which it should focus attention, while ignoring the rest. In other words, Dimensionality Reduction. …
Companies can know their customers and act on that information through effective Personalization. Realizing the importance of Big Data, Machine Learning and Analytics for delivering relevant Customer Experience — is gaining momentum at Enterprise’s Senior Executive Levels.
The ability of Big Data to provide highly personalized, cross-platform consumer experiences has the potential to lead to massive revenue increases.
Reinforcement learning is type of machine learning algorithm for learning what to do i.e. how to map situations to actions — so as to maximize a numerical reward signal. The learner is not told what actions to take, but instead must discover which actions yield the most reward by trying them.
Actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards.
Trial-and-error search and delayed reward — are the two most important distinguishing features of reinforcement learning.
A full mathematical specification of the reinforcement learning problem is in terms of optimal control of Markov decision processes. …
“The Simpsons” needs no introduction. At 29 seasons and counting, it’s the longest-running scripted series in the history of American television. This blog covers the Network Analysis of “The Simpsons” Season 4 , using Gephi an open-source graph visualization tool.
The show’s longevity, and the fact that it’s animated, provides a vast and relatively unchanging universe of characters to study. It’s easier for an animated show to scale to hundreds of recurring characters; without live-action actors to grow old or move on to other projects.
The Datasets are from the co-appearance of cast of season 4 of “The Simpsons” TV soap. The Dataset comprises of two data files provided for “The Simpsons” TV Soap for Season4…
Dynamic pricing or price optimization is the concept of offering goods at different prices which varies according to the customer’s demand. The pricing of the commodity can be done on the basis of competitor’s pricing, supply, demand and conversion rates and sales goals. The practice of Dynamic Pricing is being widely adopted in E-Commerce.
Machine learning algorithms should be able to efficiently automate pricing decisions to maximize proﬁts, as they can perform pricing decisions using sophisticated calculations and predictions, by putting all available data into perspective, and change their pricing strategy to best adapt to a dynamic environment.
Consequently, a considerable amount of research trying to solve these hard pricing problems by machine learning has been performed, and many predict that pricing done by machine learning algorithms is the future of…
From Applications to the Limitations, from the Mathematics to the underlying Algorithm — this blog intends to provide the basic understanding of Artificial Neural Networks (ANN’s).
Some of the earliest learning algorithms we recognize today were intended to be computational models of biological learning i.e. models of how learning happens or could happen in the brain. As a result, one of the names that Deep Learning has gone by is “Artificial Neural Networks” (ANN’s).
The perspective of Deep Learning Models is that they are engineered systems inspired by the biological brain.
Some of the Use Cases of ANN’s are listed…
Outliers can drastically bias/change the fit estimates and predictions. It is left to the best judgement of the analyst to decide whether treating Outliers is necessary and how to go about it.
In this blog, we’d address the topic of Outlier Detection using a case study on the Cardiotocography (CTG) Data Set from the UCI Machine Learning Repository.
Cardiotocography (CTG) in medical terms is a technical means of recording the fetal heartbeat and uterine contractions during pregnancy, typically in the third trimester. This gives information on the uterine contractions, Baseline fetal Heart Rate and other measures necessary for the Obstetrician to gauge the well-being of the fetus and suggest possible time of delivery. An improper CTG measure could suggest possibility of having to deliver through Caesarean section. …