What you'll learn
Have a great intuition of many deep learning models
Make robust deep learning models
Master deep learning with python

Description
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

What kind of problems does deep learning solve, and more importantly, can it solve yours? To know the answer, you need to ask a few questions:

What outcomes do I care about? In a classification problem, those outcomes are labels that could be applied to data: for example, spam or not_spam in an email filter, good_guy or bad_guy in fraud detection, angry_customer or happy_customer in customer relationship management. Other types of problems include anomaly detection (useful in fraud detection and predictive maintenance of manufacturing equipment), and clustering, which is useful in recommendation systems that surface similarities.

Screenshots

Citación :

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