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5 Scikit-Learn Must-Know Hidden Gems


What's better, there is no need to keep track of x-train, x-test, y-train, and y-test variables. The only downside to cross-validation is that it takes more time -- but better results always have higher costs. Although Scikit-learn is a staple in machine learning, many are not aware or do not use some of the library's most helpful hidden gems. Here are five of Scikit-learn's most useful hidden gems. Scikit-learn has plenty of dataset generators, which can be used to create artificial datasets with varying complexities and shapes.

Dropout in Neural Network


We discussed the overfitting problem last time. Now let's understand the dropout from above plots. The plots above are showing models for classifying dogs and cats. As a human being, I can easily distinguish a cat and a dog, no matter it is wearing a mask or in red or not. So I will assume that in the above plot, the mask of the cat or the red cloth of the dog is a noise since those are not specific features for the animal species.

Deep Learning Instance Segmentation Networks


In my previous article I have dealt with semantic segmentation and its networks. In this paper I will introduce instance segmentation and the network that can be used in the task of instance segmentation. One of the most important things to keep in mind is the apparent difference between semantic and instance segmentation in computer vision. As shown in figure below, in semantic segmentation, every object of the same class is coloured with the same colour. However, in instance segmentation, every object of the same class is coloured in different colours.

The Metaverse Is Coming And It's A Very Big Deal


So what happens when the world becomes a billboard, robots have spatial reasoning and virtual assistants own the relationship with the consumer? The metaverse is coming and it's a very big deal.

I turned off autocorrect on my iPhone and learned a terrible lesson


I knew I coukd do it. I blame Microsoft, of course. Samsung offers a range of smartphones with the A-series, S-series, Note line, and new foldable Android smartphones. Recently, despite my risible faithfulness to Hotmail, Microsoft's AI has been desperately trying to finish my sentences for me. This has gone beyond trying to anticipate mere words.

Q&A: UN's Agnes Callamard on drone strike that killed Soleimani

Al Jazeera

The United Nations's special rapporteur on extrajudicial, summary and arbitrary killings presented a new report to the Human Rights Council in Geneva. Agnes Callamard's investigation focused on the legality of armed drones including one that killed Iranian General Qassem Soleimani near Baghdad's airport on January 3. It concluded the United States acted unlawfully in carrying out the attack. The US, meanwhile, denounced her findings. Callamard spoke to Al Jazeera about her probe and the future of drone warfare.

Augmented Intelligence is the New Intelligence


The future of decision-making includes an inventive blend of information, analytics, and artificial intelligence (AI), with the perfect scramble of human judgment. The outcome is augmented intelligence, where the analytical force and speed of AI assumes control over most of data processing, controlling human workers to make progressively agile, more intelligent choices and find new discoveries. The development of analytics has caught the consideration of the heads of significant organizations. However, regardless of progressions throughout the years, few have had the option to stay aware of how to utilize analytics and AI among employees, in processes, and with appropriate oversight. The outcome is a lot of smart thoughts and technologies, however, applications that miss the mark regarding their potential.

First Step Towards AI


Let's cleave the process involved roughly into 2 parts, Before getting started, make sure you have created a virtual environment and installed all dependencies mentioned in previous section.( Flask is a web based microframework. Data scientists prefer flask for their application development as it is lightweight and needs very little code to convert python function into a HTTP endpoint. Assuming, the readers have prior knowledge about libraries and functions used for data analysis and building machine learning models. I will be explaining the elements or functions that are solely used for web app creation from here on.

Writing your first hello world in machine learning with Python


Machine Learning is the process of taking data as input, identifying the trends and patterns in data and giving a program as output. This program also or model is the representation of the patterns that define the data. New data can be given as input to this model/program and it will be able to classify it or make predictions on it. Python has been found to be a simple and easy to learn language that works very well with requirements of machine learning. Python has been designed to favor data analysis.

Why Programmers Are Not Data Scientists (and Vice Versa)


Hot jobs go in waves, and not surprisingly, the information technologies sector is as prone to following fashions as religiously as teenagers. There is a good reason for this, of course. The hot IT jobs are where the money is, and if you want to play in that market, then you need to have the skills or training to participate. Otherwise, you run the risk of watching your income fall as you're relegated to lesser paying jobs, or worse, are forced into IT management, doomed never to touch a compiler again, while never quite managing to play in the big leagues with the C Suite (I may be exaggerating a bit here, though not necessarily by much). Over the years the role of programmers as generalists havs faded even as their importance as tool creators to assist others has grown dramatically.