Machine Learning

One of the easiest ways to get the last juice out of the models is to pick the right hyperparameters for machine learning or deep learning models. I will show you in this article some of the best ways to do hyperparameter tuning available today (in 2021) Difference between parameter and hyper-parameters? Parameters of the model: These are the parameters calculated on the given dataset by the model. The weights of a deep neural network, for instance. Hyperparameters of Models: these are the parameters where the data model cannot predict. This is used for calculating the parameters of the model. For starters, in deep neural networks the learning rate. Why Hyper-parameter tuning is more important? The tuning technique is used to estimate the best hyperparameter combination that helps the algorithm to optimise the efficiency of the model. The proper hyperparameter combination is the only way to achieve the full value from the models. How to Choose Hyper-parameters? It isn’t a straightforwa...

  Would you let an Artificial Intelligence make decisions on behalf? — If Yes, then to what extent, maybe your life depends on it. From the incredibly-friendly voice of Apple’s personal assistant, Siri, to movies like Ex-Machina, Al has always excited me more than anything else. The very idea that Netflix can actually predict a recommendation list of movies based on your reaction to a previously seen movie sounds fascinating to me and with this approach I have been working on Machine Learning Algorithms and its all classifiers to make much more robust and easy to understand by everyone, So I started uploading all the basic Machine Learning Algorithms From 10 March 2020 to 10 August 2020 on my Github Repository in Python Programming and R Programming. Then one day out of nowhere I come across a video on YouTube by Siraj Raval, in which he talked about something called #100DaysOfMLCode Challenge. It means coding and studying machine learning for at least an hour, every day for the ne...

I enjoy reading and spending time browsing Medium and writing in it. I use it as my blog, as my go-to source for exciting news, opinions, idea center, topic reading, a lot.  But writing and sharing information and getting around in Artificial Intelligence is not necessarily very easy. Using Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing and other buzzing words effectively can be a little complex as it keeps changing itself. Fortunately, Blogs gave us a lot of information on all the aspects and Cheatsheets plays a very vital role in the field of learning and memorizing things. They have excellent tips and tricks and “how-to” articles but they are all over the place. I spent one month putting together all cheat sheets for  Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing all together to work effectively and get to know every useful information . Feel free to use it, critique it, recommend, add to it, etc...

The way that R-squared shouldn’t be utilized for choosing if you have a satisfactory model is illogical and is once in a while clarified unmistakably. This exhibit diagrams how R-squared integrity of-fit functions in relapse investigation and relationships while demonstrating why it’s anything but a proportion of measurable sufficiency, so ought not to propose anything about future prescient execution. The R-squared Goodness-of-Fit measure is one of the most broadly accessible insights going with the yield of relapse investigation in factual programming. Maybe incompletely because of its far-reaching accessibility, it is additionally one of the frequently misjudged ones. Initial, a concise update on R-squared (R2). In a relapse with a solitary free factor, R2 is determined as the proportion between the variety clarified by the model and the all-out watched variety. It is regularly called the coefficient of assurance and can be deciphered as the extent of variety clarified by the presen...

Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other? Neural networks  have been shown to outperform a number of machine learning algorithms in many industry domains. They keep learning until it comes out with the best set of features to obtain a satisfying predictive performance. However, a neural network will scale your variables into a series of numbers that once the neural network finishes the learning stage, the features become indistinguishable to us. If all we cared about was the prediction, a neural net would be the de-facto algorithm used all the time. But in an industry setting, we need a model that can give meaning to a feature/variable to stakeholders. And these stakeholders will likely be anyone other than someone with a knowledge of deep learning or machine learning. What’s the Main Difference Between the random forest algorithm and Neural N...

Python’s popularity is still backed by a rock-solid community of computer scientists, data scientists, and AI specialists. But if you have ever been at a dinner table with these people, you also know how much they rant about the weaknesses of Python. From being slow to requiring excessive testing, to producing runtime errors despite prior testing — there is enough to be pissed off about. Therefore more and more programmers are adopting other languages — the top players being Julia, Go, and Rust.  Julia is great for mathematical and technical tasks, while Go is awesome for modular programs, and Rust is the top choice for systems programming. Since data scientists and AI specialists deal with lots of mathematical problems, Julia is the winner for them. And even upon critical scrutiny, Julia has upsides that Python cannot beat. Why Python is not the programming language of the future When people create a new programming language, they do so because they wa...