Author Archives: 365Data Science

Overcoming the Racial Bias in AI

The results of any AI developed today is entirely dependent on the data on which it trains. If the data is distributed–intentionally or not–with a bias toward any category of data over another, then the AI will display that bias. What is a better way forward to handle this possibility toward bias when the datasetsContinue reading “Overcoming the Racial Bias in AI”

How to Become a Marketing Analyst?

A marketing analyst is one of the key figures within any company. Marketing analysts not only help businesses utilize market data as a strategic tool to develop new products but they also interpret consumer behavior, refine business ideas, and even assess the viability of entering a new competitive sector. Naturally, the position has amazing potentialContinue reading “How to Become a Marketing Analyst?”

Building Neural Networks with PyTorch in Google Colab

Combining PyTorch and Google’s cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that. Originally from KDnuggets https://ift.tt/3jPKow9 source https://365datascience.weebly.com/the-best-data-science-blog-2020/building-neural-networks-with-pytorch-in-google-colab

Seven Steps for Migrating Sensitive Data to the Cloud: A Guide for Data Teams

Cloud migration requires a careful planning process to ensure all systems work as they should. Use this checklist, sponsored by Immuta and TDWI, to learn seven best practices for data teams migrating sensitive data to the cloud. Originally from KDnuggets https://ift.tt/3kS4Mh6 source https://365datascience.weebly.com/the-best-data-science-blog-2020/seven-steps-for-migrating-sensitive-data-to-the-cloud-a-guide-for-data-teams

Uncertainty and Prediction in Model-based Reinforcement Learning

Reinforcement Learning (RL) provides a mathematical formalism for learning-based control. In Deep Reinforcement Learning (DRL), a neural network with reinforcement learning is used to enhance the algorithm the ability to control the system with extremely high-dimensional input spaces such as images [1]. Learning from limited samples is one of the challenges which can be facedContinue reading “Uncertainty and Prediction in Model-based Reinforcement Learning”

Empirical Portfolio TheoryMultiple Asset Case with Dashboard

Building and simulating different stock portfolio and assessing risk and return with Python and R Shiny Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/empirical-portfolio-theory-multiple-asset-case-with-dashboard-8169911fff23?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/empirical-portfolio-theorymultiple-asset-case-with-dashboard

Dont give up on machine learning because of your low-end hardware

Year by year machine learning models are getting more advanced, more accurate and break performance records. We collect more data and we… Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/dont-give-up-on-machine-learning-because-of-your-low-end-hardware-d284eec4a9ef?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/dont-give-up-on-machine-learning-because-of-your-low-end-hardware

Explaining the Explainable AI: A 2-Stage Approach

Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. Even more so, explaining any type of understanding of AI models to humans is yet another challenging layer that must be addressed if we are to develop a complete approach to Explainable AI. Originally fromContinue reading “Explaining the Explainable AI: A 2-Stage Approach”

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