RNNs are built in such a way that they can learn from data sequences by sending the hidden state from one step in the sequence to the next, together with the input. LSTMs are a step forward from RNNs and are employed when a neural network has to flip between remembering recent and distant events.

Assume there is an image recognition neural network, and the following image is fitted. …


Driver fatigue has become one of the key reasons for road accidents in modern days. Various surveys prove that if a driver is correctly identified as fatigued, and he or she is timely alarmed regarding the same, the cases of accidents can be remarkably reduced. There have been various techniques adopted to identify a drowsy driver. Through this project, an in-depth study of various existing techniques of fatigue in a driver is studied, followed by developing a deep learning-based model to accurately identify a driver’s state using a novel technique of using spatiotemporal features of the face. …


One of the best things about using Python is its infinity of open-source libraries. There is a library for basically anything. If a library can solve a problem, why not save your precious time and give it a try? Today, I will introduce you to 5 libraries that you probably have never heard about but you should add to your pipeline. Let’s get started!

PyForest

When you start typing your code for a project, what is your first step? You probably import the libraries you will need, right? The problem is that you never know how many libraries you will need…


Python is one of the most popular programming languages among developers. It is used everywhere, whether it’s web development or machine learning.

There are many reasons for its popularity, such as its community support, its amazing libraries, its wide usage in Machine Learning and Big Data, and its easy syntax.

Despite having these many qualities, python has one drawback, which is its slow speed. Being an interpreted language, python is slower than other programming languages. Still, we can overcome this problem using some tips.

In this article, I will share some python tricks using which we can make our python…


Are you a fresher with no/little work experience? Are you not confident enough to get a job in this field? Then this post will help you in improving your profile and work on skills that are needed to get started with.

I have been in the same boat and struggled a lot in getting an internship. I had received tons of rejections after applying to 50+ jobs every day, went through a lot of interviews including a couple of final rounds, and got ghosted by recruiters as well. I know it sucks but even my journey has just begun and…


Recently Microsoft announced the Power BI integration with Jupyter, Now we can tell data stories using Jupyter notebook.

As Microsoft launching a new power client package we can export and embed Power BI reports, dashboards, dashboard tiles, report visuals or Q&A in Jupyter notebooks easily.

Power BI is a set of software services, apps, and connectors that work together to transform data from different data sources into logical, visually immersive, and interactive insights.

Our data could be in the form of an Excel spreadsheet or a collection of hybrid data warehouses that are both cloud-based and on-premises.

Power BI makes…


Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” -Josh Wills, Director of Data Engineering at Slack.

We stand in midst of a deluge of data today. Starting from the smartphone in your palm to the smart refrigerator at your home, it’s everywhere. Today, over 2.5 quintillion bytes of data is generated every day, which is expected to rise up to 463 exabytes by 2025. Even though the systems that generate these vast volumes of data expire in a matter of time, the data doesn’t. …


Let’s face it, even before we were properly exposed to data science we had probably heard both of these terms: overfitting and underfitting. The reason these two terms shall be regarded as the guiding philosophy of machine learning is that every machine learning model in existence conforms to the trade-off between both of these, which in turn dictates their performance and therefore every machine learning algorithm seeks to create models that offer the best trade-off between them.

But why do we care about it?

Whenever we model any data using machine learning, the end objective is that the trained model should be able to correctly predict the…

Qusai Onali

Multifaceted Geek | Brand Ambassador @ IEEE | Strategist | Analyst | Engineer | Writer

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