Applied Deep Learning A Case-Based Approach to Understanding Deep Neural Networks /

• Κύριος συγγραφέας: Michelucci, Umberto (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2018.
• Έκδοση: 1st ed. 2018.
• Θέματα: Artificial intelligence.; Python (Computer program language).; Open source software.; Computer programming.; Big data.; Artificial Intelligence.; Python.; Open Source.; Big Data.

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 sing...

Πίνακας περιεχομένων:

• Chapter 1: Introduction
• Chapter 2: Single Neurons
• Chapter 3: Fully connected Neural Network with more neurons
• Chapter 4: Neural networks error analysis
• Chapter 5: Dropout technique
• Chapter 6: Hyper parameters tuning
• Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.)
• Chapter 8: Convolutional Networks and image recognition
• Chapter 9: Recurrent Neural Networks
• Chapter 10: A practical COMPLETE example from scratch (put everything together)
• Chapter 11: Logistic regression implement from scratch in Python without libraries. .