Pragmatic machine learning toolkit @ AWS platform

Machine Learning library Path And Package’s

Machine Learning has emerged as a revolutionary technology for numerous firms today. It’s a tech innovation that ensures swift development of predictive applications. Business owners can leverage the technology and gain crucial insights into organizational data. The analysis and reports will surely help them make informed moves and decisions.

With the help of machine learning, entrepreneurs can perform significant processes such as demand forecasting, fraud detection, and data analytics. Machine Learning comes with reliable insights and powerful algorithms that boost the identification of existing data patterns from existing data sets. These patterns will also help data scientists derive new insights from new information and data sets. Successful data extraction and analytics can be the key to ensuring huge business profits. That’s not all; organizations can identify and solve critical operational issues too.

If you wish to leverage the power of traditional ML and Artificial Intelligence, Pragmatic Machine Learning Toolkit will prove to be the most reliable partner. Data scientists can work on popular ML libraries in Amazon Cloud. Organization owners can scale up their processes and Pentaho Instance size according to their performance needs.

Frequently Asked Questions

User Manual

Installed Libraries

Frequently Asked Questions: FAQ

1] What is the use of Pragmatic Deep Learning and Machine Learning Toolkit?

Pragmatic Deep Learning and Machine Learning Toolkit finds application in the creation of predictive applications. You can use the toolkit for building applications for flagging suspicious transactions, detecting fraudulent orders, forecasting demands, predicting user activities, filtering reviews, analyzing free texts, recommending items, and listening to social media feeds

2] What are the benefits of Pragmatic Deep Learning and Machine Learning Toolkit?

Pragmatic offers Pragmatic Deep Learning and Machine Learning Toolkit on AWS marketplace. With some of the major ML libraries including Panda, NLTK, Theano, Tensorflow, Torch, Gensim, Elastics, Spark, and CNTK, customers can reap the benefits of single-click server launch

3] What is the Pragmatic Support?

By launching an email helpdesk for Pragmatic Deep Learning and Machine Learning Toolkit, Pragmatic helps users solve issues while using and configuring ML libraries. We pride on our associations with efficient data scientists and ML experts, who will understand your problems and offer the perfect solutions to those issues. With in-depth knowledge of Python and ML algorithms, they can work on any predictive analysis issue

4] What are the services offered by Pragmatic Deep Learning and Machine Learning Toolkit?

The technology is designed for 24*7 availability. Users won’t come across scheduled maintenance or downtimes. The evaluation, batch prediction, and model training API run on Amazon’s secured, highly reliable, and proven data centers. Even if there’s an ‘availability zone outage’ or ‘server failure,' the toolkit will provide optimum fault tolerance with service-stack replications

5] What are the charge’s to launch Pragmatic Deep Learning and Machine Learning Toolkit on AWS Cloud?

There are no charges involved in the process

6] What is the Instance type for Pragmatic Deep Learning and Machine Learning Toolkit on AWS Cloud?

It’s T2 Medium with a storage capacity of 50GB

7] What are the key features of the Toolkit?

Here are the essential features of this innovative toolkit:

OS: Ubuntu 16.04 LTS
Installed Libraries: Pandas, NLTK, Scikit-learn, Theano, TensorFlow, CAFFE, TORCH, Spark, Gensim, Elastics, CNTK
Python: - Python2/3

8] What are Pragmatic Deep Learning and Machine Learning Toolkit and data science?

You want to draw conclusions from your data that help you solve a particular problem. The typical skills of a data scientists are

ML algorithms work on data models. It does not formulate manual rules but learns the entire data model. As a combination of computer science, mathematics, statistics, computational, and quantitative analyses, data science provides the impetus for better and improved decision making. ML or Machine Learning principles are integral parts of ‘data science’ projects. It finds application in the discovery of clustering algorithms and exploratory analysis. Data engineering happens to be a significant part of data science too, and it involves the collection, cleaning, and wrangling of crucial data sets. Data scientists will draw inferences from specific data sets that help them solve particular issues. The standard and common skills of data scientists include

Computer Science: Programming, hardware expertise
Math: Calculus, Linear algebra, Statistics
Communication: Presentation and Visualization
Domain knowledge

User Manual

Johnny-Five

Johnny_five library path
Library name:- node_modules
ubuntu@ip-x-x-x-x:~$ cd /opt/

Now open your text editor and create a new file called "johnny_five_test.js", in that file type or paste the following:

var five = require("johnny-five"),

      board = new five.Board();

      

      board.on("ready", function() {

      // Create an Led on pin 13

      var led = new five.Led(13);

      

      // Strobe the pin on/off, defaults to 100ms phases

      led.strobe();

      });

Make sure the board is plugged into your host machine (desktop, laptop, raspberry pi, etc). Now, in your terminal, type or paste the following:
Should run below command through root privilege or sudo users
ubuntu@ip-x-x-x-x:~$ sudo node johnny_five_test.js

After run the above js script. Success should look like this

Pandas

/usr/local/lib/python2.7/dist-packages/pandas

      #sudo pip install xlrd

      #sudo pip install xlwt

      #sudo pip install openpyxl

      #sudo pip install XlsxWriter

Scikit-learn

/usr/local/lib/python2.7/dist-packages/scikit_learn-0.18.1.dist-info

      /usr/local/lib/python2.7/dist-packages/scikit_image-0.12.3.dist-info

Testing requires having the nose library. After installation, the package can be tested by executing from outside the source directory:
nosetests -v sklearn

NLTK

/usr/lib/python2.7/dist-packages/nltk

Theano

/usr/local/lib/python2.7/dist-packages/theano
python `python -c "import os, theano; print(os.path.dirname(theano.__file__))"`/misc/check_blas.py
run the Theano/BLAS speed test:

CAFFE

/opt/caffe

TensorFlow

/usr/local/lib/python2.7/dist-packages/tensorflow

TORCH

Generating OpenBLASConfigVersion.cmake in /opt/OpenBLAS/lib/cmake/openblas
Install OK!

/opt/torch

      /home/ubuntu/.bashrc

Spark

/usr/lib/spark/python/pyspark

Gensim

/usr/local/lib/python2.7/dist-packages/gensim

Elastics

/usr/share/elasticsearch/lib

CNTK

/usr/local/CNTKCustomMKL

To activate the CNTK environment, run
source "/home/ubuntu/cntk/activate-cntk"

Please checkout tutorials and examples here:

/home/ubuntu/cntk/Tutorials

      /home/ubuntu/cntk/Examples

Installed Libraries

Johnny-Five

As a JavaScript Robotics program, Johnny Five helps coders write and read to and from the Arduino board with the help of JavaScript. It’s open source nature and uncomplicated API resembles jQuery, which makes it highly familiar amongst programmers. The library comes with pre-built components that share compatibility with all types of servo, sensors, button, LED boards you can think of. If you want to work with it on Raspbian, the entire process will be tricky.

Pandas

Programmers wishing to perform swift data manipulation and analysis can use the Pandas Python Library. It’s imperative to perform the explorations properly, where the first step will include data reading and printing of crucial summary statistics. The Pandas library seems to be the best option in this context. By providing data analysis tools and structures, Pandas helps you perform data manipulation quite easily and effectively. The standard data structure in Pandas is known as a data frame, which is an extended version of the matrix. We need to talk about and understand a matrix before discussing data frames.

CNTK

CNTK has two definitions. Earlier, the name was ‘Computational Network Toolkit, ’ and then it came to be known as ‘Microsoft Cognitive Toolkit.' If you wish to perform deep and regular neural network analysis, nothing can get better than CNTK. As a command-line program and Microsoft’s internal tool, CNTK undergoes swift and rapid development. The incomplete and improper documentation makes the entire framework quite weak.

Although there’s a tremendous competition between CNTK and Google’s TensorFlow tool, skilled coders and programmers prefer the former framework to the latter. As far as program documentation is concerned, both these tools need improvement. Since it runs on Windows, CNTK offers better functionality and operability than its competitor.

Scikit-learn

If you wish to leverage the power of Machine Learning, nothing can get better than Scikit-learn, a user-friendly and popular Python package. The library comprises of a complete range of unsupervised and supervised learning algorithms through a consistent Python interface. With a simplified and permissive BSD license and numerous Linux distributions, Scikit-Learn encourages commercial and academic use. SciPy modules and extensions are known as Scikits. As such, the module is termed as scikit-learn and provides learning algorithms.

NLTK

As a platform or framework for creating Python programs, the ‘Natural Language Toolkit’ or NLTK helps programmers work with human data. It contains interfaces for a whopping 50 lexical resources and corpora like WordNet. Other than that, this particular toolkit happens to be the home for diverse components such as NLP languages, text processing libraries for tokenization, classification, tagging, parsing, stemming, and semantic reasoning.
Originally developed at the Pennsylvania University, NLTK finds application in diverse modules and courses in 32 universities worldwide. Some of the highlights of this particular library include:

Lexical analysis
Text-and-word tokenization
Collocations and n-gram
Parts-of-speech tagging
Text-chunker
Tree Model
Named-entity recognition

The framework works for OSX, Windows, and Linux. It also requires Python 3.2, 2.7, or later versions. You can also check the online book for special support.

TensorFlow

Deep neural networks and Machine Learning are the two most magical solutions for Google. When it comes to using the ML package, Google uses TensorFlow, powered by TPUs or Tensor Processing Units in its datacenters. The Google Brain Team is the key force behind developing TensorFlow. The program released in November 2015 to open-source technologies. TensorFlow ensures smooth computation by leveraging scalable ML techniques and data-flow graphs. The presence of nodes signifies mathematical operations, while graph edges show multidimensional tensors or data arrays. If you want to deploy computation to one or more GPUs or CPUs in a server, desktop, or mobile device, the flexible and dynamic architecture of TensorFlow will help you do that.

Theano

As a dynamic and multidimensional Python Library, Theano helps developers optimize, define, and evaluate mathematical expressions, specially the one with a multi-dimensional array. The framework was developed and created at the Montreal University in the LISA Lab. It finds application in supporting computationally-intensive scientific investigations since the year 2007. Montreal University uses this framework in its deep learning and ML classes.

CAFFE

Developed by the ‘Berkeley Vision and Learning Center,' Caffe happens to be a deep learning framework. The core framework for Caffe is developed in C++ and licensed under the BSD-2 Clause. It also receives support from the CUDA framework on Nvidia GPUs and can switch between random GPUs and CPUs. Caffe works on Python, Matlab, and command-line interfaces.

TORCH

With a GPU-first technology, the Torch computing framework offers wide support for ML algorithms. The presence of underlying C/CUDA implementations and LuaJIT scripting language makes the framework highly efficient and easy-to-use. Some of the unique highlights of the framework include:

Computer Vision
Community-created packages
Signal processing
Image and video processing
Parallel processing
Networking
Builds with the Lua community

Spark

Developers and coders who wish to work with high-level APIs in Scala, Python, Java and R can choose Apache Spark. As a general-purpose and swift cluster computing system, Apache Spark supports execution graphs. The framework supports high-quality tools like structured data processing and Spark SQL, GraphX for graph processing, MLlib for ML processing, and Spark Streaming.

Gensim

Another innovative framework is Gensim, which is a free library for Python developed to extract semantic topics from documents. The framework has the capability to carry out the entire process efficiently, effortlessly, and swiftly. Gensim also finds application in the processing of unstructured and raw digital texts. Some of the high-powered algorithms like ‘Latent Dirichlet Allocation,' ‘Latent Semantic Analysis,' and ‘Random Projections’ are present in Gensim and can identify a document’s semantic structure by examining statistical co-occurrence of lexical items and their patterns.

Elastics

Elastic happens to be an open-source solution capable of solving a growing list of log analysis, search analysis, and analytics challenges. The technology finds application across numerous virtual industries. Our commercial monitoring and security products take our open-source stack further by extending the horizon and broadening the possibilities of data. Developers will experience optimum convenience while working on these dynamic frameworks.