The recent advantage of computing power has enabled us to make far stretched steps into delving right into the future with technologies like Artificial Intelligence and machine learning.
The introduction of computer vision has enabled us to conquer very big strides into real-time object detection. Let us see in this article how computer vision will help us to conquer the future.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. …
Even though the topic is irrelevant to trained machine learning experts, This age-old debate has always been in the spot-light for beginners who are stepping into the AI world.
Even though the conclusion is inconclusive(*wink*) let us try to settle the debate on which one to start with and which one is more useful for beginners and intermediate.
Both machine learning and deep learning are forms of artificial intelligence, however, with some notable differences. While machine learning is a specific application of AI, deep learning is a distinctive form of ML.
In order to make the most out of them…
If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn.
Scikit-learn is an open-source Python library that implements a range of machine learning, preprocessing, cross-validation, and visualization algorithms using a unified interface.
Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.
It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.
The library is built…
Given the fact that it’s one of the fundamental packages for scientific computing, NumPy is one of the packages that you must be able to use and know if you want to do data science with Python. It offers a great alternative to Python lists, as NumPy arrays are more compact, allow faster access to reading and writing items, and are more convenient and more efficient overall.
In addition, it’s (partly) the fundament of other important packages that are used for data manipulation and machine learning which you might already know, namely, Pandas, Scikit-Learn, and SciPy:
For most beginners, the first Python data visualization library that they use is, naturally, Matplotlib. It is a Python 2D plotting library that enables users to make publication-quality figures. It is quite an extensive library where a cheat sheet will definitely come in handy when you’re learning, but when you manage to use this library effectively, you’ll also be able to get insights and work better with other packages, such as Pandas, that intend to build more plotting integration with Matplotlib as time goes on.
Another package that you’ll be able to tackle easily is Seaborn, the statistical data visualization…
Data never sleeps and in today’s world, without utilizing the wealth of digital information available at our fingertips, a brand or business risks missing vital insights that can help it grow, scale, evolve, and remain competitive.
That said, to spare you any confusion and offer you a clearcut insight into these two innovative fields, here we explore data science vs data analytics in a business context, starting with an explanation.
TensorFlow is developed by Google Brain and actively used at Google both for research and production needs. Its closed-source predecessor is called DistBelief.
PyTorch is a cousin of lua-based Torch framework which was developed and used at Facebook. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native.
When data doesn’t fit in memory, we can use chunking: loading and then processing it in chunks, so that only a subset of the data needs to be in memory at any given time. But while chunking saves memory, it doesn’t address the other problem with large amounts of data: computation can also become a bottleneck.
How can we speed processing up?
One approach is to utilize multiple CPUs: pretty much every computer these days has more than one CPU. …
You need the experience to get experience. This seems to be the biggest issue for young adults transitioning into the workforce these days.
A practical work background carries a major significance when attempting to enter the job market. It’s all about competition.
Generally, an internship is a task-specific exchange of service for experience between a student and a business. Within internships, classroom concepts suddenly become real tools of the trade as you interact and learn in a professional setting. Internship experiences are formal, formative, and foundational to your career.
Developing your knowledge of workplace collaboration, business etiquette, and strong communication…
In this article let me share my experience of working with synthetic image generation during my tenure as an intern in Tactii and Tactlabs.
Synthetic data generation is just artificial generated data in order to overcome a fixed set of data availability by the use of algorithms and programming. While dealing with datasets containing images.
There are several ways out there to create synthetic image datasets, and in this article, we will look into one of the most used methods to generate synthetic images — Image Composition. …
Intern at Tactii and Tactlabs. Aviation geek, Computer Science enthusiast