Most of the machine learning algorithms work well due to predefined representations and input features. Machine learning algorithms optimize weights to best make a final prediction, while representation learning attempt to automatically learn good features or representations. Deep learning algorithms attempt to learn at multiple levels of representation by increasing complexity. Deep architectures are composed of multiple levels of non-linear operations, such as neural nets with many hidden layers. The main goal of deep learning techniques is to learn feature hierarchies. Deep learning techniques can be divided into three major classes; deep networks for unsupervised or generative learning, deep networks for supervised learning and hybrid deep networks
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You're reading from Practical Machine Learning Cookbook
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
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Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Read more about Atul Tripathi