Category Archives: Data Science News

In this article I would like to focus on how companies can start their data-centric strategies and how to achieve success in their data transformation journeys. Have tried to share my thoughts why companies have to consider data at its epitome for their growth, for being competitive, for being smarter, innovative and be prepared forContinue reading “Data is everywhere and it powers everything we do!”

Source Deep Learning An Overview What is Deep Learning? Deep learning is a subset of machine learning, which in turn is a subset of Artificial intelligence. Artificial intelligence involves traditional methods to learn from data, whereas machine learning involves teaching the computer to recognize pattern from the data. Deep learning is a machine learning techniqueContinue reading “Deep Learning- An Overview”

Convolutional Neural Networks (CNNs) have achieved significant success in many computer vision tasks. In recent years, they have reduced… Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/reducing-spatial-redundancy-in-cnn-with-octave-convolution-98307355fbb0?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/reducing-spatial-redundancy-in-cnn-with-octave-convolution

10 Ways How AI can Transform the Education Industry The advent of artificial intelligence in the education industry has benefited both students and teachers. The merger of this technology with today’s education system is revolutionizing the way students understand a concept and learn new things. Besides this, the technology has also changed the way teachers usedContinue reading “10 Ways How can AI Transform the Education Industry?”

With more advancements in AI, it might be time to replace the age-old Turing Test with something better to determine if a machine is thinking. Specifically, a more modern approach might include standard questions designed to probe various facets of intelligence, and comparing the computer to a spectrum of human respondents of different ages, sexes,Continue reading “Beyond the Turing Test”

Inspired by Judea Pearl’s do-calculus for causal inference, the open source framework provides a programmatic interface for popular causal inference methods. Originally from KDnuggets https://ift.tt/31z0UdW source https://365datascience.weebly.com/the-best-data-science-blog-2020/microsofts-dowhy-is-a-cool-framework-for-causal-inference

With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have. Originally from KDnuggets https://ift.tt/31xMg6u source https://365datascience.weebly.com/the-best-data-science-blog-2020/explainable-and-reproducible-machine-learning-model-development-with-dalex-and-neptune

Predicting whether a passenger survived. RMS Titanic. Source: https://theculturetrip.com/europe/united-kingdom/articles/how-the-titanic-changed-the-world/. Welcome back! In my previous post I wrote an EDA (Exploratory Data Analysis) on Titanic Survival dataset. Check it out now if you haven’t already. Anyway, in this article I would like to be more focusing on how to create a machine learning model which isContinue reading “Titanic Survival Dataset Part 2/2: Logistic Regression”

Finding out important (and probably interesting) information in the dataset. RMS Titanic. Source: https://id.wikipedia.org/wiki/RMS_Titanic. Hi everyone, Ardi here! In this article I wanna do Exploratory Data Analysis (EDA) on Titanic dataset. So far, I’ve been doing several projects in which most of those are related to classification on unstructured data (i.e. image classification). Today, instead ofContinue reading “Titanic Survival Dataset Part 1/2: Exploratory Data Analysis”

Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow. Originally from KDnuggets https://ift.tt/2FXQiNb source https://365datascience.weebly.com/the-best-data-science-blog-2020/4-ways-to-improve-your-tensorflow-model-key-regularization-techniques-you-need-to-know