The Resource Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)

Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)

Label
Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks
Title
Applied Deep Learning
Title remainder
A Case-Based Approach to Understanding Deep Neural Networks
Statement of responsibility
by Umberto Michelucci
Creator
Author
Author
Subject
Language
eng
Summary
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. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. 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). You will: Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset
http://library.link/vocab/creatorName
Michelucci, Umberto
Dewey number
006.31
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
7TnCcmMoIgQ
LC call number
Q334-342
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Python (Computer program language)
  • Open source software
  • Computer programming
  • Big data
  • Artificial Intelligence
  • Python
  • Open Source
  • Big Data
Label
Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)
Instantiates
Publication
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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.
Dimensions
unknown
Edition
1st ed. 2018.
Extent
1 online resource (425 pages)
Form of item
online
Isbn
9781484237908
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-1-4842-3790-8
Sound
unknown sound
Specific material designation
remote
System control number
  • (CKB)4100000006519785
  • (MiAaPQ)EBC5510213
  • (DE-He213)978-1-4842-3790-8
  • (EXLCZ)994100000006519785
Label
Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)
Publication
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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.
Dimensions
unknown
Edition
1st ed. 2018.
Extent
1 online resource (425 pages)
Form of item
online
Isbn
9781484237908
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-1-4842-3790-8
Sound
unknown sound
Specific material designation
remote
System control number
  • (CKB)4100000006519785
  • (MiAaPQ)EBC5510213
  • (DE-He213)978-1-4842-3790-8
  • (EXLCZ)994100000006519785

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