Source: neptune.ai

This article was originally written by Aigiomawu Ehiaghe and posted on the Neptune blog.

Processing can be used to improve the quality of your image, or to help you extract useful information from it. It’s useful in fields like medical imaging, and it can even be used to hide data inside an image.

In this article I’ll tell you about how Image Processing can be applied in Machine Learning, and what techniques you can use. First, let’s explore more real world examples of Image Processing.

Image processing in the real world

In medicine, scientists study the inner structures and tissues of organisms in order to help…


Source: neptune.ai

This article was originally written by Asritha Bodepudi and posted on the Neptune blog.

Have you ever tried working with a large dataset of machine learning or deep learning algorithms on Jupyter Notebooks? Well, then you probably ran into the all — too familiar and annoying ‘memory-error.’


Source: neptune.ai

This article was originally written by Nelson Ogbeide and posted on the Neptune blog.

Tourism has enjoyed massive growth over the years, as people seek to spend time away from home in pursuit of recreation, relaxation, and pleasure. At least before COVID times, tourism has been a fast-growing sector that plays a big role in the global economy.

According to the United Nations World Tourism Organization, there were an estimated 25 million international arrivals in 1950. 68 years later, it grew to about 1.4 billion international arrivals, an approximately 56 fold increase.

According to Statista, travel, and tourism directly contributed…


Source: neptune.ai

This article was originally written by Gopal Singh and posted on the Neptune blog.

Machine learning is increasingly popular and the increasing number of companies are harnessing the power of this new technology. Yet the knowledge about the teams themselves is yet limited — what do they use? What do they like? Who are they?

Neptune has been built by data scientists for data scientist — no mumbo-jumbo in between. So when facing the challenge of missing knowledge we do the best we can do — gather the data. …


This article was originally written by Stephen Oni and posted on the Neptune blog.

Earlier this year (2020), I decided to move fully into the engineering part of machine learning from Data Science. I wanted to experience a more efficient and scalable way of deploying machine learning models, decouple my models from my app, and version them properly.

Conventionally, what I do mostly after training my model is to import the model in my flask app and then perform inference whenever the API endpoint for the model is being called. …


Source: neptune.ai

This article was originally written by Vidushi Meel and posted on the Neptune blog.

Jupyter notebooks are cool. They’re language-independent, great for collaboration, easy to customize, you can add extensions — the list goes on.

Issues begin when you need to track training hyperparameters, metrics, test results, or graphs. That’s when the chaos starts.

Then there are spreadsheets, which can quickly become unmanageable, especially in a team environment where you have multiple people who need to edit them at the same time.

Managing spreadsheets is painful, and not great for productivity. …


Source: neptune.ai

This article was originally written by Kamil Kaczmarek and posted on the Neptune blog.

Last week I participated in the ECML-PKDD 2020 Conference. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases is one of the most recognized academic conferences on ML in Europe.

In the spirit of spreading the word about ML developments, I wanted to share my selection of the best “applied data science” papers from the conference. It is the second post from this series. The previous one about top research papers, can be found here. …


Source: neptune.ai

This article was originally written by Piotr Januszewski and posted on the Neptune blog.

Based on simply watching how an agent acts in the environment it is hard to tell anything about why it behaves this way and how it works internally. That’s why it is crucial to establish metrics that tell WHY the agent performs in a certain way.

This is challenging especially when the agent doesn’t behave the way we would like it to behave, … which is like always. …


Source: neptune.ai

This article was originally written by Vitaliy Lyalin and posted on the Neptune blog.

Image processing is a very useful technology and the demand from the industry seems to be growing every year. Historically, image processing that uses machine learning appeared in the 1960s as an attempt to simulate the human vision system and automate the image analysis process. As the technology developed and improved, solutions for specific tasks began to appear.

The rapid acceleration of computer vision in 2010, thanks to deep learning and the emergence of open source projects and large image databases only increased the need for…


Source: neptune.ai

This article was originally written by Vladimir Lyashenko and posted on the Neptune blog.

In machine learning (ML), generalization usually refers to the ability of an algorithm to be effective across various inputs. It means that the ML model does not encounter performance degradation on the new inputs from the same distribution of the training data.

For human beings generalization is the most natural thing possible. We can classify on the fly. For example, we would definitely recognize a dog even if we didn’t see this breed before. Nevertheless, it might be quite a challenge for an ML model. …

Growth Specialist at @neptune_ai

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