11 most read Deep Learning Articles from Analytics Vidhya in 2017
Introduction This is that time of year, when you reflect on the year gone by. 2017 has been a year of growth for us at …
Fundamentals of Deep Learning – Introduction to Recurrent Neural Networks
Introduction Let me open this article with a question – “working love learning we on deep”, did this make any sense to you? Not …
2 days to go for the DataHack Summit
Introduction What do you think of when you hear the word “Summit” or “Conference”? You think, knowledgeable talks and theoretical buzz. Research and case …
Fundamentals of Deep Learning – Activation Functions and When to Use Them?
Introduction Internet provides access to plethora of information today. Whatever we need is just a Google (search) away. However, when we have so much …
30 Questions to test a Data Scientist on Deep Learning (Solution – Skill test, July 2017)
Introduction Whether you are a novice at data science or a veteran, Deep learning is hard to ignore. And it deserves the attention, as …
Architecture of Convolutional Neural Networks (CNNs) demystified
Introduction I will start with a confession – there was a time when I didn’t really understand deep learning. I would look at the …
Transfer learning and the art of using Pre-trained Models in Deep Learning
Introduction Neural networks are a different breed of models compared to the supervised machine learning algorithms. Why do I say so? There are multiple …
25 Must Know Terms & concepts for Beginners in Deep Learning
Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years. …
41 questions on Statistics for data scientists & analysts
Introduction Statistics forms the back bone of data science or any analysis for that matter. Sound knowledge of statistics can help an analyst to …
40 Questions on Probability for data science
Introduction Probability forms the backbone of many important data science concepts from inferential statistics to Bayesian networks. It would not be wrong to say …
