Strata Beijing - Deep Learning in Production on SparkAdam Gibson
Recent talk at strata beijing - half english half chinese covering use cases of deep learning, deep learning in production and the different components of deeplearning4j.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Strata Beijing - Deep Learning in Production on SparkAdam Gibson
Recent talk at strata beijing - half english half chinese covering use cases of deep learning, deep learning in production and the different components of deeplearning4j.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
David Kale and Ruben Fizsel from Skymind talk about deep learning for the JVM and enterprise using deeplearning4j (DL4J). Deep learning (nouveau neural nets) have sparked a renaissance in empirical machine learning with breakthroughs in computer vision, speech recognition, and natural language processing. However, many popular deep learning frameworks are targeted to researchers and poorly suited to enterprise settings that use Java-centric big data ecosystems. DL4J bridges the gap, bringing high performance numerical linear algebra libraries and state-of-the-art deep learning functionality to the JVM.
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Advanced Spark and TensorFlow Meetup 08-04-2016 One Click Spark ML Pipeline D...Chris Fregly
Empowering the Data Scientist with "1-Click" Production Deployment and Canary Testing of High-Performance and Highly-Scalable Spark ML and TensorFlow Models directly from Jupyter/iPython Notebooks using Docker, Kubernetes, Netflix OSS, Microservices, and Spinnaker.
With proper tooling and metrics, Data Scientists can directly deploy, analyze, A/B test, rollback, and scale out their Spark ML and TensorFlow model into live production serving with zero friction.
We will show you the open source tools that we've built based on Docker, Kubernetes, Netflix Open Source, Microservices, Spinnaker - and even Chaos Monkey!
Speaker: Chris Fregly @ PipelineIO, formerly Databricks and Netflix
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
Time-series forecasting of indoor temperature using pre-trained Deep Neural N...Francisco Zamora-Martinez
Artificial neural networks have proved to be good at time-series forecasting
problems, being widely studied at literature. Traditionally, shallow
architectures were used due to convergence problems when dealing with deep
models. Recent research findings enable deep architectures training, opening a
new interesting research area called deep learning. This paper presents a study
of deep learning techniques applied to time-series forecasting in a real indoor
temperature forecasting task, studying performance due to different
hyper-parameter configurations. When using deep models, better generalization
performance at test set and an over-fitting reduction has been observed.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Advanced Spark and Tensorflow Meetup - London - Nov 15, 2016 - Deploy Spark M...Chris Fregly
YouTube Video: https://www.youtube.com/watch?v=RnnweVC7wFc
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
David Kale and Ruben Fizsel from Skymind talk about deep learning for the JVM and enterprise using deeplearning4j (DL4J). Deep learning (nouveau neural nets) have sparked a renaissance in empirical machine learning with breakthroughs in computer vision, speech recognition, and natural language processing. However, many popular deep learning frameworks are targeted to researchers and poorly suited to enterprise settings that use Java-centric big data ecosystems. DL4J bridges the gap, bringing high performance numerical linear algebra libraries and state-of-the-art deep learning functionality to the JVM.
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Advanced Spark and TensorFlow Meetup 08-04-2016 One Click Spark ML Pipeline D...Chris Fregly
Empowering the Data Scientist with "1-Click" Production Deployment and Canary Testing of High-Performance and Highly-Scalable Spark ML and TensorFlow Models directly from Jupyter/iPython Notebooks using Docker, Kubernetes, Netflix OSS, Microservices, and Spinnaker.
With proper tooling and metrics, Data Scientists can directly deploy, analyze, A/B test, rollback, and scale out their Spark ML and TensorFlow model into live production serving with zero friction.
We will show you the open source tools that we've built based on Docker, Kubernetes, Netflix Open Source, Microservices, Spinnaker - and even Chaos Monkey!
Speaker: Chris Fregly @ PipelineIO, formerly Databricks and Netflix
Gentlest Introduction to Tensorflow - Part 2Khor SoonHin
Video: https://youtu.be/Trc52FvMLEg
Article: https://medium.com/@khor/gentlest-introduction-to-tensorflow-part-2-ed2a0a7a624f
Code: https://github.com/nethsix/gentle_tensorflow
Continuing from Part 1 where we used Tensorflow to perform linear regression for a model with single feature, here we:
* Use Tensorboard to visualize linear regression variables and the Tensorflow network graph
* Perform stochastic/mini-batch/batch gradient descent
Time-series forecasting of indoor temperature using pre-trained Deep Neural N...Francisco Zamora-Martinez
Artificial neural networks have proved to be good at time-series forecasting
problems, being widely studied at literature. Traditionally, shallow
architectures were used due to convergence problems when dealing with deep
models. Recent research findings enable deep architectures training, opening a
new interesting research area called deep learning. This paper presents a study
of deep learning techniques applied to time-series forecasting in a real indoor
temperature forecasting task, studying performance due to different
hyper-parameter configurations. When using deep models, better generalization
performance at test set and an over-fitting reduction has been observed.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Advanced Spark and Tensorflow Meetup - London - Nov 15, 2016 - Deploy Spark M...Chris Fregly
YouTube Video: https://www.youtube.com/watch?v=RnnweVC7wFc
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
翻轉學習的四大基礎— Four Pillars:F . L . I . P
Flexible Environments(彈性的學習環境)
Learning Culture(不同的學習文化)
Intentional Content(更明確的內容)
Professional Educators(更專業的教師)
-----------------------------
能仁家商翻轉教育講座
Program Guide: Let Agile Fly! Scrum Gathering Shanghai 2012 ConferenceShining Hsiong
This is the program guide for the coming Let Agile Fly: Scrum Gathering Shanghai 2012 Conference, to be held on June 7~9 (conference), and June 6 (tutorial), in Shanghai, Beijing.
This is the fifth time that China agile funs organize such a big community conference. Last year's Scrum Gathering brings more than 400 delegated from China as well as the world. This year we will bring Scrum Gathering to an even bigger success!
Some of the most world influential agile experts will come and speak in the conference, such as Jurgen Appelo, Lyssa Adkins, and Craig Larman. Rich and profound programs will be designed by the community with no bias and no commercial purpose. The estimated size of the conference is 500~600 attendees per day. With the largest CS*s gathering in China, you can’t miss the chance to expand your connections in China, and even in Asia.
Read: issuu.com/shuweigoh/docs/skymind
At Skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. We offer state-of-the-art, flexible, scalable deep learning for industry. Deep learning is becoming an important tool set for natural-language processing (NLP), computer vision, database predictions, pattern recognition, image/video processing and fraud detection.
Warm Mix Asphalt - Paving the Green WayShu Wei Goh
Field Evaluation of Warm Mix Asphalt - A technology that allowed the producers of Hot-Mix Asphalt (HMA) pavement material to lower the temperatures at which the material is mixed and placed on the road.
16. 让企业运用深度学习
● 不管拥有大数据或小数据,都可以方便的部署深度学习
Ability to work with small and big data easily
○ 避免为了升级到大数据系统(HADOOP)时把原本的机器学习工具都换掉
● 避免花费时间在数据矢量化与抽取、转换、装载(ETL)
Ability not to get caught up things like Vectorization and ETL
○ 专注于开发更好的深度学习模型
● 可以同时间实验、训练更多的深度学习模型
Ability to experiment with lots of models
○ 同时也要避免为了把深度学习部署到生产线时需要重新编辑机器学习工具