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An introduction to Machine Learning with Brain.js

#artificialintelligence

In this post, we'll look at some machine learning concepts and learn more about Brain.js.We will discuss some aspects of understanding how neural networks work. We will learn terms like forward and backward propagation along with some other terms used in the machine learning community. Then we will leverage on the power of Brain.js to build a day to day meeting scheduling application using a constitutional neural network. Using Brain.js is a fantastic way to build a neural network. It learns the patterns and relationship between the inputs and output in order to make a somewhat educated guess when dealing with related issues. One example of a neural network is Cloudinary's image recognition add-on system.


Boosting and Bagging: How To Develop A Robust Machine Learning Algorithm

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Machine learning and data science require more than just throwing data into a python library and utilizing whatever comes out. Data scientists need to actually understand the data and the processes behind the data to be able to implement a successful system. One key methodology to implementation is knowing when a model might benefit from utilizing bootstrapping methods. These are what are called ensemble models. Some examples of ensemble models are AdaBoost and Stochastic Gradient Boosting.


Fujitsu develops AI that captures high-dimensional data characteristics - IT-Online

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Fujitsu Laboratories has developed what it believes to be the world's first AI technology that accurately captures essential features, including the distribution and probability of high-dimensional data in order to improve the accuracy of AI detection and judgment. High-dimensional data, which includes communications networks access data, types of medical data, and images remain difficult to process due to its complexity, making it a challenge to obtain the characteristics of the target data. Until now, this made it necessary to use techniques to reduce the dimensions of the input data using deep learning, at times causing the AI to make incorrect judgments. Fujitsu has combined deep learning technology with its expertise in image compression technology, cultivated over many years, to develop an AI technology that makes it possible to optimize the processing of high-dimensional data with deep learning technology, and to accurately extract data features. It combines information theory used in image compression with deep learning, optimising the number of dimensions to be reduced in high-dimensional data and the distribution of the data after the dimension reduction by deep learning.


The journey that organizations should embark on to realize the true potential of AI

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Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a'pure play' technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation – (1) AI is a'portfolio' based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many'standalone' technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are'specialists' in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things – 'Define Ambitious and Achievable Success Criteria', 'Develop the Right Operating Rhythm', and'Create and Celebrate Success Stories' to realize the true potential of AI. Most companies have very narrow or ambiguous'success criteria' definition of their AI program.


6 Dimensionality Reduction Algorithms With Python

#artificialintelligence

Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm. In this tutorial, you will discover how to fit and evaluate top dimensionality reduction algorithms in Python.


The 10 Best AI And Data Science Undergraduate Courses For 2021

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Artificial Intelligence is the hottest topic in technology and commerce today, and the field of data science is fundamental to how it works. Courses in data science all now contain a strong AI presence, and a few institutions are already offering specialized undergraduate degrees in AI. The increasing number of colleges and universities offering courses in these subjects indicates industry-wide expectations that there will be a world of rewarding opportunities for those with formal training and accreditation. Well, according to Glassdoor.com the average salary last year for a data scientist stood at $107,000. So, it's certainly a career worth considering if earning a good starting wage is on your list of priorities!


Interview with Tim Schwuchow, Data Science Lead at Premise Data

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We thank Tim Schwuchow from Premise Data for taking part in the part of the Data Science Interview Series 2020. I spent too much in grad school and did coding projects on my own and loved doing my own statistical analysis. I fell in love with it and then got involved with Premise. We've been doing crazy, exciting things and I'm lucky to have a lot of autonomy. I have a lot of trust and power and am allowed to find ways to make the data useful.


Innovative Education at IIMT

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IIMT College of Engineering is showing its beautiful academic face proudly to attract the inquisitive young minds to fulfil their dreams of becoming a successful engineering graduate. IIMT has got a shining face not by only applying the latest management gloss. It has not created a fetish of ranking in NCR as a ritual only. Actually IIMT college of Engineering has rewritten the contract between the engineering education and the striving middle class Indian society. It has rewritten the solidarity between faculty and students. It has believed in age old guru-shishya tradition.


Parents' unwillingness to impose boundaries 'spawned a generation of infantilised millennials'

Daily Mail - Science & tech

Parents not enforcing boundaries and being unwilling to chastise children has led to a generation of'infantilised millennials', according to a sociology professor. In his book, Why Borders Matter, Frank Furedi, emeritus professor of sociology at Kent University, says a lack of clear boundaries has created a childlike generation. Not chastising children or using moral-based judgements'deprives them of a natural process' of fighting against parental rules and boundaries, says Furedi. He says children develop by reacting against boundaries given to them by parents and society, and over three or four generations those parameters have weakened. This has led to millennials in their twenties acting the way they did in their teenage years and refusing to embrace adulthood, he explained in his book.


Framework for Data Preparation Techniques in Machine Learning

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There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.