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You're reading from  Applied Deep Learning with Keras

Product typeBook
Published inApr 2019
Reading LevelIntermediate
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ISBN-139781838555078
Edition1st Edition
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Authors (3):
Ritesh Bhagwat
Ritesh Bhagwat
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Ritesh Bhagwat

Ritesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.
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Mahla Abdolahnejad
Mahla Abdolahnejad
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Mahla Abdolahnejad

Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans.
Read more about Mahla Abdolahnejad

Matthew Moocarme
Matthew Moocarme
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Matthew Moocarme

Matthew Moocarme is an accomplished data scientist with more than eight years of experience in creating and utilizing machine learning models. He comes from a background in the physical sciences, in which he holds a Ph.D. in physics from the Graduate Center of CUNY. Currently, he leads a team of data scientists and engineers in the media and advertising space to build and integrate machine learning models for a variety of applications. In his spare time, Matthew enjoys sharing his knowledge with the data science community through published works, conference presentations, and workshops.
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Introduction


In the previous chapter, we discussed some applications of machine learning and even built models with the scikit-learn Python package. In this chapter, we will continue learning how to build machine learning models and extend our knowledge to build an Artificial Neural Network (ANN) with the Keras package. (Remember that ANNs represent a large class of machine learning algorithms that are so called because their architecture resembles the neurons in the human brain.)

Keras is a machine learning library designed specifically for building neural networks. While scikit-learn functionality spans a broader area of machine learning algorithms, the functionality of scikit-learn for neural networks is minimal.

ANNs can be used for the same machine learning tasks as other algorithms that we have encountered, such as logistic regression for classification tasks, linear regression for regression problems, and k-means for clustering. Whenever we begin any machine learning problem, to determine...

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Applied Deep Learning with Keras
Published in: Apr 2019Publisher: ISBN-13: 9781838555078

Authors (3)

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Ritesh Bhagwat

Ritesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.
Read more about Ritesh Bhagwat

author image
Mahla Abdolahnejad

Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans.
Read more about Mahla Abdolahnejad

author image
Matthew Moocarme

Matthew Moocarme is an accomplished data scientist with more than eight years of experience in creating and utilizing machine learning models. He comes from a background in the physical sciences, in which he holds a Ph.D. in physics from the Graduate Center of CUNY. Currently, he leads a team of data scientists and engineers in the media and advertising space to build and integrate machine learning models for a variety of applications. In his spare time, Matthew enjoys sharing his knowledge with the data science community through published works, conference presentations, and workshops.
Read more about Matthew Moocarme