Deep Learning: An Introduction
Breandan Considine
examples
- ImageNet http://image-net.org/
- voice recognition
- big data
- hardware (nvidia cuda)
- algorithms
- tensors: n-dimensional array
- learning types:
- supervised
- unsupervised
- reinforced
- linear regression: single line in data
- classification
- "perceptron"
- layers + weighs "gradient descent"
- clustering, separation, association
- clustering: random points, euclidean distance
- feature scalin,g normalization
- decomposition & aggregation
- dimensionality reduction
- --> training set, validate & select best modelf
- builder pattern
- components:
- nd4j -- n-dimensional arrays reinforcement learning
- agent has context + choices
- rewards
- goal: maximize cumulative reward
- http://www.asimovinstitute.org/neural-network-zoo/
- "Deep learning for Java": https://deeplearning4j.org/ & https://deeplearning4j.org/documentation
- mnist samples of handwriting: http://yann.lecun.com/exdb/mnist/
- combine multiple models https://github.com/google/inception & https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html -awsome public datasets: https://github.com/caesar0301/awesome-public-datasets
Make CSS Fun Again with Flexbox!
Hubert Sablonnière
refs:
- https://css-tricks.com/snippets/css/a-guide-to-flexbox/
- autocomplete: http://emmet.io/
- autoprefixer: https://autoprefixer.github.io/
- float styles are only for text flow
- display: flex: // flex: 1 ( for 1 row)
- parent / flexcontainer
- children: elements
- flex-grow: weight
- flex-shrink: minimum
- flex-basis: default
- justify-content: center / flex-end
- align-items: center
Easily secure your Front and back applications with KeyCloak
Sébastien Blanc
http://www.keycloak.org/
Open Source Identity and Access Management
- jwt (rfc 7519)
- openid, kerberos etc
- adapters (wildfly, spring boot, node.js ...)
- native clients
- login brokers
- otp
- web.xml: roles/ security config
- keycloak.json config
- atom editor https://atom.io/
- node.js: add keycloak.protect()
Sentiment analysis of social media posts using Apache Spark
Niels Dommerholt
http://spark.apache.org/ dataprocessing engine
- positive / negative
- java 8 / streams api
- coursera
- JavaSparkContext: config
Apache Spark? If only it worked
Marcin Szymaniuk
origin: http://blog.explainmydata.com/2014/05/spark-should-be-better-than-mapreduce.html*
details
- RDD: Resilient Distributed Dataset :
- cache
- no priority
- sizing executors: configure memory (should autobalance in recent versions)
- known pitfalse: 2g block limit, gc's -- check level of parallelism (groupByKey, repartition)
- check locality: NODE_LOCAL -- increase exectors if needed
- broadcast variable
- avoid groupbykey -- use reducebykey
- debugging:
- log aggregation
- hdfs monitoring logging
- gclogs
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