A toolkit for Making Real World Machine Learning & Data analysis Apps

Dlib C++ library

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.

Compiling dlib C++ example programs

Go into the examples folder and type:

mkdir build; cd build; cmake .. ; cmake --build .

That will build all the examples. If you have a CPU that supports AVX instructions then turn them on like this:

mkdir build; cd build; cmake .. -DUSE_AVX_INSTRUCTIONS=1; cmake --build .

Doing so will make some things run faster.

Finally, Visual Studio users should usually do everything in 64bit mode. By default Visual Studio is 32bit, both in its outputs and its own execution, so you have to explicitly tell it to use 64bits. Since it's not the 1990s anymore you probably want to use 64bits. Do that with a cmake invocation like this:

cmake .. -G "Visual Studio 14 2015 Win64" -T host=x64 

Compiling your own C++ programs that use dlib

The examples folder has a CMake tutorial that tells you what to do. There are also additional instructions on the dlib web site.

Alternatively, if you are using the vcpkg dependency manager you can download and install dlib with CMake integration in a single command:

vcpkg install dlib

Compiling dlib Python API

Before you can run the Python example programs you must compile dlib. Type:

python setup.py install

Running the unit test suite

Type the following to compile and run the dlib unit test suite:

cd dlib/test
mkdir build
cd build
cmake ..
cmake --build . --config Release
./dtest --runall

Note that on windows your compiler might put the test executable in a subfolder called Release. If that's the case then you have to go to that folder before running the test.

This library is licensed under the Boost Software License, which can be found in dlib/LICENSE.txt. The long and short of the license is that you can use dlib however you like, even in closed source commercial software.

dlib sponsors

This research is based in part upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under contract number 2014-14071600010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government.

Download Details:

Author: Davisking
Source Code: https://github.com/davisking/dlib 
License: BSL-1.0 license

#machinelearning #python #cpluplus 

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Exploratory Data Analysis in Few Seconds

EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.

EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.

There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.

About Pandas Visual Analysis

1. It is an open-source python library used for Exploratory Data Analysis.
2. It creates an interactive user interface to visualize datasets in Jupyter Notebook.
3. Visualizations created can be downloaded as images from the interface itself.
4. It has a selection type that will help to visualize patterns with and without outliers.

Implementation

1. Installation
2. 2. Importing Dataset
3. 3. EDA using Pandas Visual Analysis

Understanding Output

Let’s understand the different sections in the user interface :

1. Statistical Analysis: This section will show the statistical properties like Mean, Median, Mode, and Quantiles of all numerical features.
2. Scatter Plot-It shows the Distribution between 2 different features with the help of a scatter plot. you can choose features to be plotted on the X and Y axis from the dropdown.
3. Histogram-It shows the distribution between 2 Different features with the help of a Histogram.

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4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

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Maintenance / Support / Sustenance

Integration / Data Management

QA & Automation

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