Deep Learning – is yet another buzz word and is the successor of Machine Learning and now a predecessor to Artificial Intelligence or simply put – AI. The intriguing thing is the number of articles that exist in our enterprise ecosystem, all emphatically pressing on the implementation of deep learning to solve real world problems. However most of them restricts themselves to uncomfortable algebra and calculus which too seems to be “inspired” from Stanford Courses (In case you are not aware of those, try googling CS231n and CS224d, they are awesome)
The challenging thing however is the capability to solve real “Business” problems. I quoted business because there is plethora of tutorials using Deep Learning to solve problems like Computer Vision for Auto driven cars or someone More…
Hi there , This blog Post will tell you how to tune R using different techniques, so lets get started:
We Would use the Sonar Dataset. This is the data set used by Gorman and Sejnowski in their study of the classification of sonar signals using a neural network. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
Each pattern is a set of 60 numbers in the range 0.0 to 1.0.
This is one of the webinars that I took to educate students, industry professionals who were looking to make a shift in their career and that too in Data Science. Data Science is a combination of Statistics , Math and Programming. For more details please check out the video. The journey to become a data scientist has many milestones and it is important to reach each one of them one by one. Here are some of the first few steps for getting started:
Also there multiple ways / source to improve your newly acquired skills :