Actualidad Deep Learning

I am a big fan of learning through practical application. I have found that when studying machine learning, it can be really useful to obtain some publically available data sets to apply the latest technique I have learnt to. Or you might want a really simple data set to benchmark a solution or compare… https://towardsdatascience.com/free-data-sets-for-machine-learning-73e74554cc21

Dealing with imbalanced datasets is an everyday problem. SMOTE, Synthetic Minority Oversampling TEchnique and its variants are techniques for solving this problem through oversampling that have recently become a very popular way to improve model performance. https://www.datasciencecentral.com/profiles/blogs/dealing-with-imbalanced-datasets

In its rush to solve all the world’s problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today’s deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era’s focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting m

https://www.datasciencecentral.com/profiles/blogs/deep-learning-data-sets-for-every-data-scientist

Balanced Dataset : Before giving you the definition of Balanced dataset let me give you an example for your better understanding, lets assume I have a dataset with thousand data points and I name it “N”. So now N = 1000 data points, & N have two different classes one is N1 and another one is N2. Inside the N1 there have 580 data points and inside the N2 there have 420 data points. N1 have positive (+Ve) data points and N2 have negative (-Ve) data points. So we can say that the number of data points of N1 and N2 is almost similar than each other. So then I can write N1 ~ N2. Then it is proved that N is a Balanced Dataset. https://medium.com/@suvhradipghosh/imbalanced-vs-balanced-dataset-in-machine-learning-4faec5629b7e

Google is the latest company to stake its claim as king of health care technology. Streams, a tool to diagnose kidney disease, is being trialed by the UK’s National Health Service (NHS). Far more sophisticated tools are clearly in the pipeline. It recently unveiled "promising" artificial intelligence that can identify lung cancer a year before a doctor could. https://www.forbes.com/sites/forbestechcouncil/2019/07/01/should-we-give-google-our-health-care-data/#357989c3ce44