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Applied Deep Learning with Keras

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  • Understand the difference between single-layer and multi-layer neural network models
  • Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks
  • Apply L1, L2, and dropout regularization to improve the accuracy of your model
  • Implement cross-validate using Keras wrappers with scikit-learn
  • Understand the limitations of model accuracy

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

  • Solve complex machine learning problems with precision
  • Evaluate, tweak, and improve your deep learning models and solutions
  • Use different types of neural networks to solve real-world problems
Page Count 412
Course Length 12 hours 21 minutes
Date Of Publication 23 Apr 2019


Matthew Moocarme

Matthew Moocarme is a director and senior data scientist who works with Viacom’s Advertising Science team. Matthew determines and predicts the all aspects of the success of Viacom’s branded ad campaigns from data aggregation, to data integrity, to predictive modeling and forecasting. Matthew lives in New York City and outside of work enjoys combining deep learning with music theory. He is a classically trained physicist, holding a Ph.D. in Physics from The Graduate Center of CUNY.

Mahla Abdolahnejad

Mahla Abdolahnejad is a PhD candidate in Systems and Computer Engineering, Carleton University, Canada. She also holds a bachelor and a master degree in Biomedical Engineering where she was first exposed to the field of Artificial Intelligence and artificial neural networks in particular. Her PhD research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human and a machine’s way of learning from the visual world and how to push machine learning algorithms toward learning and thinking like humans. Mahla also has nearly 4 years of teaching assistant experience at university level through which she was involved in a range of assignments such as conducting group and one-to-one tutorial sessions, conducting computer lab sessions, grading, and evaluations, and helping students understand the material and debug codes.

Ritesh Bhagwat

Ritesh Bhagwat has over 14 years of experience in working on data drive technologies. His experience includes leading and contributing on 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. He currently works as a data scientist. He has a master's degree in applied mathematics with a specialization in computer science. Apart from work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in bayesian statistics as a philosophy.