Introduction to Deep Learning
Traditional machine learning models have always been very powerful to handle structured data and have been widely used by businesses for credit scoring, churn prediction, consumer targeting, and so on.
The success of these models highly depends on the performance of the feature engineering phase: the more we work close to the business to extract relevant knowledge from the structured data, the more powerful the model will be.
When it comes to unstructured data (images, text, voice, videos), hand engineered features are time consuming, brittle and not scalable in practice. That is why Neural Networks become more and more popular thanks to their ability to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.
Improvements in Hardware (GPUs) and Software (advanced models / research related to AI) also contributed to deepen the learning from data using Neural Networks.
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Sequence Modeling with Neural Networks - Part I
In the previous course, we saw how to use Neural Networks to model a dataset of many examples. The good news is that the basic architecture of Neural Networks is quite generic whatever the application: a stacking of several perceptrons to compose complex hierarchical models and optimization of these models using gradient descent and backpropagation.
Inspite of this, you have probably heard about Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTM, Auto-Encoders, etc. These deep learning algorithms are different from each other. Each model is known to be particulary performant in some specific tasks, even though, fundamentally, they all share the same basic architecture.
What makes the difference between them is their ability to be more suited for some data structures: dealing with text could be different from dealing with images, which in turn could be different from dealing with signals.
In the balance of this article, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.
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