Deep Learning for Java

Deep learning has become the next big thing in realization of Artificial Intelligence. However, many libraries and frameworks are still very much experimental and for research purposes. In realistic business applications, in order for deep learning to be a viable option it has to be scalable over Big Data. Cloud environments and massive parallelization have made such scalability requirements of Machine Learning a possibility. DL4J is an open source library much needed in the Deep Learning community. It  provides for an interesting option and an array of developer friendly neural network implementations. Whatever the domain requirements are for a business, DL4J provides a viable and accessible option towards delivering a working production ready implementation.

Deep Learning A Practitioner's Approach
Fundamentals of Deep Learning Designing Next-Generation Machine Intelligence Algorithms

Natural Language Processing

Natural Language Processing has come a long way from the past eras of rules driven approaches to utilizing more Machine Learning techniques, paving the way to even more advanced hybrid methods. The area is also quite diverse and constantly growing with active research in the community. We also find Natural Language Processing as an applied discipline for almost all web and document related extraction problems. However, there is still room for more scalable libraries and frameworks as they seem to emerge out of mainly research and at times also have restricted user licenses. Natural Language Processing applications are usually designed in a pipeline architecture. They can also utilize rich domain semantics from Linked Data ontologies, vocabularies, thesauri, or even commonsense knowledge bases. Increasingly, they are also utilizing deep learning methods. In general, there are also formal frameworks supported by industry collaborations such as UIMA for building entire pipelines. Or, even frameworks like Gate that provide a variety of pluggable libraries for different domain cases and tasks in the pipeline. The following are some interesting libraries in the domain area that could be applied for Natural Language Processing applications.

solr	java
elasticsearch	java
coreNLP	java
gate	java
jgibbLDA	java
kea/maui	java
lingPipe	java
minorthird	java
openNLP	java
sphinx	java
spotlight	java
weka	java
tika	java
carrot2	java
UIMA	java
wordfreak	java
cleartk	java
dkpro	java
balie	java
simpleNLG	java
openCCG	java
glossary	javascript
lingo	javascript
natural	javascript
nlp-node	javascript
pos-js	javascript
reds	javascript
tfidf	javascript
conceptnet	python
gensim	python
nltk	python
pattern	python
textblob	python
rake	python
spacy	python
scalaNLP	scala
linkgrammar	c

Golang machine learning and NLP

Mind Mapping

Mind mapping and brainstorming tools come in handy for visually working out relationships between ideas and concepts. They often elucidate our thoughts towards more plausible and realistic outcomes. Mind mapping tools are also useful to information architects in structuring out information flows towards concepts within a domain context. Often brainstorming exercises are the best way of working out all the corner cases of a knowledge representation on data. The whole process can also provide a way of discovering new connected ideas and storyboarding before formalizing into an implementation strategy. The below provides links to a few mind mapping tools.

Mindjet

XMind

Coggle

FreeMind

MindNode

TheBrain

SimpleMind

Omnigraffle

Gelphi

Online CI Providers

Hosted Continuous Integration is a hot area but also a very competitive domain. While some choose to have it hosted in the cloud others like to have more corporate autonomy with using such tools as Jenkins and TeamCity. Continuous Integration is an agile work flow practice that involves developers to integrate on code, in shared repositories, and utilize automated tests to verify for build quality, in order to allow teams to check for issues, early and often, on a daily basis. A step further in the Continuous Integration process is Continuous Delivery. Continuous Delivery being the hardest bit to fully achieve on a large complex architecture and may even prove to be foolhardy. Although, CI has been around for years, it really boils down to team dynamics and whether one really has the time to manually setup and monitor builds in comparison to a hosted option. In some corporate environments, teams may even have a dedicated team member for build and configuration management. The following is a list of a few hosted Continuous Integration providers and the different use cases that they provide for an agile software engineering process. 

TravisCI
DroneIO
CircleCI
BuildHive/CloudBees
SemaphoreApp
ShiningPanda
Hosted-CI
Bamboo
CodeShipIO
MagnumCI
SnapCI
SolanoLabs
ShipIO
Shippable
Wercker
Appveyor
ZeroCI
dployIO

Comparison of continuous integration software