-
Easily Deploy Deep Learning Models in Production
Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.
-
Five Command Line Tools for Data Science
You can do more data science than you think from the terminal.
-
P-values Explained By Data Scientist
This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.
-
7 Tips for Dealing With Small Data
At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.
-
 Top 13 Skills To Become a Rockstar Data Scientist
Education, coding, SQL, big data platforms, storytelling and more. These are the 13 skills you need to master to become a rockstar data scientist.
-
High-Quality AI And Machine Learning Data Labeling At Scale: A Brief Research Report
Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?
-
Is SQL needed to be a data scientist?
As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.
-
Kaggle Kernels Guide for Beginners: A Step by Step Tutorial
This is an attempt to hold the hands of a complete beginner and walk them through the world of Kaggle Kernels — for them to get started.
-
What’s the Best Data Strategy for Enterprises: Build, buy, partner or acquire?
Every large organization is investing heavily in building data solutions and tools. They are building data solutions from scratch when they could be taking advantage of readily available tools and solutions. Many organizations are re-inventing the wheel and wasting resources.
-
 Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning
This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning.
|
Top 13 Skills To Become a Rockstar Data Scientist
Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning