It’s one of the technique in which we implement KFold cross-validation, where k is equal to n i.e the number of observations in the data. Thus, every single point will be used in a validation set, we will create n models, for n-observations in the data. Each point/sample is used once as a test set while the remaining data/samples form the training set.
For almost a decade, Stack Overflow’s annual Developer Survey has been the largest survey of people who code around the world. This year they did a survey on 60,000+ software developers and shared some of the amazing statistics.
In this article, I’m going to write a detailed analysis of their survey result. So get ready to read some amazing analysis —
This network graph shows which technologies are most highly correlated with each other. The size of each circle corresponds to the proportion of survey respondents who use the technology tool.
Ruby and Rails tend to get used together. Similarly…
HackerEarth, with a community of around 4 million+ developers all around the world, is a highly sought after tech platform to hire quality developers. The coding problems on this platform are very exciting and challenging which can help you build a solid foundation on various topics like data structures and algorithms, data science and to say the least you can also participate in the different world class Hackathons. I have been crunching data for past few years and just two days back read this amazing survey that Hacker Earth has done where 16000 developers participated from 76 nations.
I love Data. Period.
Like every day (30 minutes reading ritual before starting my office work), I was going through some great data science and machine learning articles to keep myself abreast of latest trends/info and I found a great survey that was done by HackerRank.
In this post, I’m going to cover the trends in the developers’ community in 2020. Let’s dive in!
The results indicate that Larger companies are more likely to want to hire specialists as compared to smaller companies (startups) which considers full-stack developers more important.
Recently I received an email from one of my readers asking me to write about Python’s complex topics such as Iterators, Generators, and Decorators. In this post, I’m going to cover the basics, implementation, and how to use them in your code.
An iterator is an object that can be iterated upon which means that you can traverse through all the values. List, tuples, dictionaries, and sets are all iterable objects.
To create an object as an iterator you have to implement the methods
__next__() to your object where —
__iter__() returns the iterator object itself. …
Keras is a very powerful open source Python library which runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques.
There are multiple stages to running machine learning algorithms as it involves a sequence of tasks including pre-processing, feature extraction, model fitting, performance and validation. Pipeline is nothing but a technique through which we create linear sequence of data preparation and modeling steps to automate machine learning workflows. An automated pipeline consists of components and how those components can work together to produce and update the machine learning model. In this post, we are going to create pipeline, find best scalar, estimators and see accuracy score of different machine learning algorithms. …
Recurrent Neural Networks (RNN) initially created in the 1980’s are a powerful and robust type of neural network in which output from the previous step are fed as input to the current step. The most important feature of RNN is Hidden state and they have memory which remembers each and every information through time. In Recurrent neural networks, we use the result obtained through the hidden layers to process future input as shown in the diagram below.
Preferred algorithm for sequential data — financial data, audio, video, time series data, speech, text, etc
Clustering is a technique of dividing the population or data points, grouping them into different clusters on the basis of similarity and dissimilarity between them. It’s helps in determining the intrinsic group among the unlabeled data points.
Keras is a very powerful open source Python library which is runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques.