Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
eBook Details:
- Paperback: 410 pages
- Publisher: WOW! eBook; 1st edition (September 8, 2018)
- Language: English
- ISBN-10: 1484237897
- ISBN-13: 978-1484237892
eBook Description:
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function.
Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).
