Machine Learning

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 Netwo

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 want to keep the good