Hands-on Tutorials

From an idea to a native application with YOLOv5, TorchServe & React Native

Photo by Kevin Ku on Unsplash

Whenever there is an article on an end-to-end deep learning project, it consists of training a deep learning model, deploying a Flask API, and then making sure it works or it extensively consists of creating a web demo using Streamlit or something similar. The problem with this approach is that it talks about a straight-forward and typical path that has been tried and tested. It merely takes replacing a single piece of the puzzle with an equivalent, such as a sentiment analysis model with a classification model, etc, and a new project can be created, but the wireframe remains mostly…


Source

When one starts off with Machine Learning, more often than not they are introduced to small-scaled, preprocessed datasets, which generally have a small number of attributes/features associated with them, but that is not the case in real life. More complex datasets available over the internet, and real ones come with a large number of data points, and various attributes associated with them. Then there is the question, why collect such a large number of attributes rather than just collecting the most relevant ones? One of the key reasons for it is that it’s not very apparent which features are relevant…


Generated vs Real Images

Although Deep Convolutional GAN has been visited time and again, for the ones reading the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks without much context it can still be a great challenge. I have always found reading a research paper and implementing it gives you a better understanding of the topic than anything else. And the idea presented here is a basic but quite interesting implementation of GAN.

The network is implemented using the Pytorch framework and trained over the MNIST dataset.

Take a look at the image above. One-half of the digits are real and the…

Himanshu Dutta

Machine Learning and Data Science Practitioner

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