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Deep learning involves training neural networks with multiple hidden layers from "summary" of Data Science for Business by Foster Provost,Tom Fawcett

Deep learning is a subset of machine learning that involves training neural networks with multiple hidden layers. These hidden layers allow the network to learn increasingly abstract features from the input data. By passing the input data through a series of layers, each of which applies a transformation to the data, the network can learn complex patterns and relationships. The key to deep learning is the ability of neural networks to automatically discover the representations needed for feature detection as they are trained on data. In traditional machine learning approaches, feature engineering is typically done by humans, who must manually select and design the features that are fed into the model. This process can be time-consuming and may not always result in the most effective features for the task at hand. By using deep learning, we can bypass the need for manual feature engineering and allow the neural network to learn the features directly from the data. This can lead to more accurate and robust models, as the network can adapt to the specific characteristics of the data it is being trained on. Training deep neural networks can be challenging, as the network must learn the appropriate weights for each connection between neurons in each layer. This process requires a large amount of labeled training data and computational resources to iterate through many training examples and adjust the weights accordingly. Despite these challenges, deep learning has shown impressive performance on a wide range of tasks, including image and speech recognition, natural language processing, and more. As computational resources continue to improve and more labeled data becomes available, deep learning is likely to play an increasingly important role in the field of data science.
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    Data Science for Business

    Foster Provost

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