The Resource Advanced deep learning with Keras, Philippe Remy
Advanced deep learning with Keras, Philippe Remy
Resource Information
The item Advanced deep learning with Keras, Philippe Remy represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.This item is available to borrow from 1 library branch.
Resource Information
The item Advanced deep learning with Keras, Philippe Remy represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.
This item is available to borrow from 1 library branch.
- Summary
- "Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We'll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks. First, we present a well-known use case of deep learning: recommender systems, where we try to predict the "rating" or "preference" that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we'll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network. Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images."--Resource description page
- Language
- eng
- Extent
- 1 online resource (1 streaming video file (5 hr., 11 min., 38 sec.))
- Note
- Title from resource description page (viewed January 26, 2018)
- Label
- Advanced deep learning with Keras
- Title
- Advanced deep learning with Keras
- Statement of responsibility
- Philippe Remy
- Language
- eng
- Summary
- "Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We'll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks. First, we present a well-known use case of deep learning: recommender systems, where we try to predict the "rating" or "preference" that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we'll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network. Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images."--Resource description page
- Cataloging source
- UMI
- Characteristic
- videorecording
- http://library.link/vocab/creatorName
- Remy, Philippe
- LC call number
- QA76.87
- PerformerNote
- Presenter, Philippe Remy
- Runtime
- 312
- http://library.link/vocab/subjectName
-
- Python (Computer program language)
- Neural networks (Computer science)
- Machine learning
- Technique
- live action
- Label
- Advanced deep learning with Keras, Philippe Remy
- Note
- Title from resource description page (viewed January 26, 2018)
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Configuration of playback channels
- unknown
- Content category
- two-dimensional moving image
- Content type code
-
- tdi
- Content type MARC source
- rdacontent
- Control code
- on1020493867
- Dimensions
- unknown
- Extent
- 1 online resource (1 streaming video file (5 hr., 11 min., 38 sec.))
- Form of item
- online
- Media category
-
- computer
- video
- Media MARC source
-
- rdamedia
- rdamedia
- Media type code
-
- c
- v
- Medium for sound
- other
- Other physical details
- digital, sound, color
- Sound
- sound
- Sound on medium or separate
- sound on medium
- Specific material designation
-
- remote
- other
- Stock number
- CL0500000933
- Video recording format
- other
- Label
- Advanced deep learning with Keras, Philippe Remy
- Note
- Title from resource description page (viewed January 26, 2018)
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Configuration of playback channels
- unknown
- Content category
- two-dimensional moving image
- Content type code
-
- tdi
- Content type MARC source
- rdacontent
- Control code
- on1020493867
- Dimensions
- unknown
- Extent
- 1 online resource (1 streaming video file (5 hr., 11 min., 38 sec.))
- Form of item
- online
- Media category
-
- computer
- video
- Media MARC source
-
- rdamedia
- rdamedia
- Media type code
-
- c
- v
- Medium for sound
- other
- Other physical details
- digital, sound, color
- Sound
- sound
- Sound on medium or separate
- sound on medium
- Specific material designation
-
- remote
- other
- Stock number
- CL0500000933
- Video recording format
- other
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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.mvlc.org/portal/Advanced-deep-learning-with-Keras-Philippe/3mMGR4-F2Qc/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.mvlc.org/portal/Advanced-deep-learning-with-Keras-Philippe/3mMGR4-F2Qc/">Advanced deep learning with Keras, Philippe Remy</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.mvlc.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.mvlc.org/">Merrimack Valley Library Consortium</a></span></span></span></span></div>
