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...

Tensorflow is an end-to-end open-source machine learning platform for everyone. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. whereas, With over 375,000 individual users as of early 2020, Keras has strong adoption across both the industry and the research community. Together with TensorFlow 2.0, Keras has more adoption than any other deep learning solution — in every vertical. You are already constantly interacting with features built with Keras — it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products. Keras & TensorFlow 2.0 are also a favorite among researchers, coming in #1 in terms of mentions in scientific papers indexed by Google Scholar. Keras has also been adopted by researchers at la...

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...