3 Best Machine Learning Framework for design, experiment and deployment

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ML / DL framework for deployment and experiment

*) Listed framework is opensource

*) It provides dashboard

*) Configure, organize, log and reproduce experiments

*) make prototyping experiments easier

Machine learning life cycle framework

List of opensource ML framework

*) MLFlow
    ** An open source platform for the complete machine learning lifecycle

    ** It currently offers three components: MLflow Tracking, MLflow Projects, MLflow Models

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*) Sacred
    ** configure, organize, log and reproduce experiments

    ** easily run your experiment for different settings

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*) FGLab
    ** machine learning dashboard, designed to make prototyping experiments easier.

    ** here are 3 ways to run FGLab: Installing locally, via Docker, or hosted on Heroku.

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If anything can be add / missed out, mention in the comments section.

In next blog will discuss possible use-case from Education AREAS FOR AI INTERVENTION.

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#AI for social media analytics

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List of social media research trend in deep learning / Artificial Intelligence

*) Most of them who using smartphone actively involved in Social Media. the social platform is one of the major sources for gathering and collecting data analysis for the researcher.

*) Here I have listed few use cases and research area from social media data.

AI for social media

List of social media usecase for AI

*) Detecting incidents of cyber bullying
    Input: text feed from social media conversations
    Output: cyber bullying victim and bully identified

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*) Characterizing mental stress and suicidal tendencies
    Input: text feed from online profiles and conversations
    Output: people suspected to have stress or suicidal tendencies are flagged

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*) Understanding the stress level of students
    Input: Social Media feed
    Output: Predicting the stress level of students and based upon stress level give them consultation

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*) Predicting the molesters and eve-teasers
    Input: Social Media feed and history
    Output: Predicting the behaviour of a person.

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*) Predicting the fake locations on social Media
    Input: Image uploaded by user
    Output: Predicting exact location

If anything can be add / missed out, mention in the comments section.

In next blog will discuss possible use-case from AREAS FOR AI INTERVENTION.

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Weka Hadoop Integration - weka read/write data from HDFS

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Weka Hadoop Integration using distributedWekaHadoop package

In weka, Tools --> PackageManager search "distributedWekaHadoop" and install the packages.

Now go back to your weka KnowledgeFlow, you can find HDFSLoader and HDFSSaver in DataLoder and DataSink portion.

by using which you can read / write data to / from HDFS

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Big Data / Data Analytics Jobs

Top 10 Deep Learning Tools for R/Python/Java/Matlab/C++

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refer - For get better idea about Deep learning

Deep Learning Tools For R

H2O - Parallel distributed machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments

Deep Learning Tools For R and Python

MXNET - Deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize efficiency and your productivity.

Neuraldesigner - Neural Designer is an innovative deep learning tool for predictive analytics. Deep learning algorithms are the most powerful method to discover intricate relationships, recognize complex patterns or predict current trends in your data.

Deep Learning Tools For Python

Theano - Allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Caffe - Caffe is a deep learning framework made with expression, speed, and modularity in mind. matcaffe – is the caffe package in caffe/matlab in which you can integrate Caffe in your Matlab code.

TensorFlow - Provides a straightforward way for users to train computers to perform tasks by feeding them large amounts of data. The software incorporates various methods for efficiently building and training simulated “deep learning” neural networks across different computer hardware.

Deep Learning Tools For Java

DL4J - The first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments, rather than as a research too.

Deep Learning Tools For C++/Scripting

Opennn - It is intended for advanced users, with high C++ and machine learning skills.

Torch - Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Elektronn - Is a deep learning toolkit that makes powerful Neural Networks accessible to scientists outside of the machine learning community. Written in Python and based on the Theano framework.

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Big Data / Data Analytics Jobs