Uncovering Anxious Deep Learning for Ease
Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is – Sir Geoffrey Hinton (Google).
The true challenge to Artificial Intelligence is to prove and solve the tasks that are easy for human to perform but hard to describe formally. Problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images. In deep learning this is the task we try to solve at AILabPage research.
A technique for implementing machine learning. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my opinion). The technique is to achieve a goal not necessarily come out of same goal.
Deep learning’s main driver are artificial neural networks system or neural networks or neural nets. There are some specialized versions also available. Such as convolution neural networks and recurrent neural networks. These addresses special problem domains. Two of the best use cases for Deep Learning which are unique as well. These are image processing and text/speech processing based on methodologies like Deep Neural Nets.
In practice Deep Learning methods, specifically Recurrent Neural Networks (RNN) models are used for complex predictive analytics. Like share price forecasting and it consist of several stages. DL also includes decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.
Deep learning is the first class of algorithms that is scalable. Performance just keeps getting better as we feed the algorithms more data. Speech/Text and image processing can make perfect robot to start with and actions based on triggers makes it the best. It has to pass basic 4 tests. Turning test i.e needs to acquire college degree, needs to work as an employee for at least 20 years and do well to get promotions and meet ASI status.
Deep Learning is not Machine Learning
The major point where DL differs from ML in its working style. ML works based on past and present figures and then take an educated guess (sort of) into the future where DL goes much beyond just the guess. It uses the data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth.
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