I am listing down some of the very basic things that will help you to progress in your machine learning journey.. 1. Get a hold over the language to be used for implementation – practice the basics till you are comfortable. 2. Practice and become comfortable with data cleaning and processing – this is essential. … Continue reading How to learn Data Science
" The two key ideas of deep learning for computer vision — convolutional neural networks and backpropagation were already well understood in 1989. The Long Short Term Memory (LSTM) algorithm, which is fundamental to deep learning for timeseries, was developed in 1997 and has barely changed since. So why did deep learning only take off … Continue reading Why deep learning? Why now?
We know that machine learning is about mapping inputs (such as images) to targets (such as the label “cat”), which is done by observing many examples of input and targets. We also know that deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations (layers) and that these data transformations … Continue reading Understanding how deep learning works:
"If quantum mechanics hasn't profoundly shocked you, you haven't understood it yet"- Niels Bohr This quote from Niels Bohr seems absolutely correct after observing the "Double Slit Experiment" Have a look and you will say, "What's the matter with the matter" https://youtu.be/aXvHfCeXd5U
Hi All, I want to discuss Classification report from sklearn in this post. It is important to keep different metrics in mind when we are both training and evaluating models. Depending on the context, certain metrics will make more sense than others. The best thing is that the Classification report summarize it very well in … Continue reading Classification report sklearn