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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)
Resource Information
The item Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries.This item is available to borrow from all library branches.
Resource Information
The item Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries.
This item is available to borrow from all library branches.
- 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
- Language
- eng
- Edition
- 1st ed. 2018.
- Extent
- 1 online resource (425 pages)
- 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.
- Isbn
- 9781484237908
- 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
- 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)
- 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)
- 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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Albert D. Cohen Management LibraryBorrow it181 Freedman Crescent, Winnipeg, MB, R3T 5V4, CA49.807878 -97.129961
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Archives and Special CollectionsBorrow it25 Chancellors Circle (Elizabeth Dafoe Library), Room 330, Winnipeg, MB, R3T 2N2, CA49.809961 -97.131878
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Libraries Annex (not open to the public; please see web page for details)Borrow it25 Chancellors Circle (in the Elizabeth Dafoe Library), Winnipeg, MB, R3T 2N2, CA49.809961 -97.131878
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Victoria General Hospital LibraryBorrow it2340 Pembina Highway, Winnipeg, MB, R3T 2E8, CA49.806755 -97.152739
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.lib.umanitoba.ca/portal/Applied-Deep-Learning--A-Case-Based-Approach-to/7inC8KPmBlE/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.umanitoba.ca/portal/Applied-Deep-Learning--A-Case-Based-Approach-to/7inC8KPmBlE/">Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.lib.umanitoba.ca/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.umanitoba.ca/">University of Manitoba Libraries</a></span></span></span></span></div>
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.lib.umanitoba.ca/portal/Applied-Deep-Learning--A-Case-Based-Approach-to/7inC8KPmBlE/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.umanitoba.ca/portal/Applied-Deep-Learning--A-Case-Based-Approach-to/7inC8KPmBlE/">Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks, by Umberto Michelucci, (electronic resource)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.lib.umanitoba.ca/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.umanitoba.ca/">University of Manitoba Libraries</a></span></span></span></span></div>
