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Seminar convention hall
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Snowking Retreat
An explanation of the Open PHACTS API, and how you can use it to help with your drug discovery workflows. Presented by Christine Chichester at the Open PHACTS Drug Discovery Workflow Workshop: http://www.openphactsfoundation.org/open-phacts-pipeline-pilot-knime-workshop/ The Open PHACTs API can be accessed at https://dev.openphacts.org/login
2015-05-19 Open PHACTS Drug Discovery Workflow Workshop - The API
2015-05-19 Open PHACTS Drug Discovery Workflow Workshop - The API
open_phacts
Careers adviser presentation, February 2016
Finding work globally 2016
Finding work globally 2016
University of Sussex, Careers & Employability Centre
This tutorial, based on a published book by Giovanni Seni, offers a hands-on intro to ensemble models, which combine multiple models into a single predictive system that’s often more accurate than the best of its components. Participants will use data sets and snippets of R code to experiment with the methods to gain a practical understanding of this breakthrough technology. Giovanni Seni is currently a Senior Data Scientist with Intuit where he leads the Applied Data Sciences team. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition and data mining applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. His book with John Elder, “Ensemble Methods in Data Mining – Improving accuracy through combining predictions”, was published in February 2010 by Morgan & Claypool. Giovanni is also an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class.
Strata 2013: Tutorial-- How to Create Predictive Models in R using Ensembles
Strata 2013: Tutorial-- How to Create Predictive Models in R using Ensembles
Intuit Inc.
How do you give a killer presentation? How do stand in front of a group with confidence and interact with them to give them best possible experience. How do build your story and make sure it is fit for your audience?
How to give a killer presentation - Corda Campus keynote kick off 2016
How to give a killer presentation - Corda Campus keynote kick off 2016
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se presenta el tema de museo como recurso didactico, su definicion, historia ademas de los tipos de museo que existe
El museo[1]
El museo[1]
UNAM
TremaDay#9のLT資料です。
ハードなOpen flowswitcの話
ハードなOpen flowswitcの話
Masayuki Miyake
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Silabo Tesis 1 EPG UNCP 2015 Adolfo Gustavo CONCHA FLORES
Silabo Tesis 1 EPG UNCP 2015 Adolfo Gustavo CONCHA FLORES
Seminar convention hall
Seminar convention hall
2015-05-19 Open PHACTS Drug Discovery Workflow Workshop - The API
2015-05-19 Open PHACTS Drug Discovery Workflow Workshop - The API
Finding work globally 2016
Finding work globally 2016
Strata 2013: Tutorial-- How to Create Predictive Models in R using Ensembles
Strata 2013: Tutorial-- How to Create Predictive Models in R using Ensembles
How to give a killer presentation - Corda Campus keynote kick off 2016
How to give a killer presentation - Corda Campus keynote kick off 2016
El museo[1]
El museo[1]
ハードなOpen flowswitcの話
ハードなOpen flowswitcの話
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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
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Thermal Engineering Unit - I & II . ppt
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Continuous integration
1.
Continuous Integration Shane Duffy
2.
We are not
reinventing the wheel
3.
Phabricator is a
collection of open source web applications that help software companies build better software.
4.
Code Review
5.
Track bugs &
tasks
6.
Git Fast performance Cheap local
branches Staging areas Multiple workflows
7.
CLI tools
Editor's Notes
Lots of great open source tools available Git http://git-scm.com/ SVN https://subversion.apache.org/ Phabricator http://phabricator.org/ Jenkins http://jenkins-ci.org/
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