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Recently, I was doing a project with relatively high volume of data. Which made me curious about the possibility of using Spark to process data in a distributed setup. Accordingly, I decided to setup Spark and get my hands dirty with PySpark (python interface for Spark). In this post, I will delve into the steps […]
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In the previous post, we have walked through the process of fitting a straight line or a polynomial line using linear regression on continues data. But, what if the output we are trying to predict is a discrete value. For example what if we are supposed to model a logical AND gate?! In this case the […]
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In the previous post, I covered the very basic implementation of a web API structure using python decorators. But, it was just the beginning of a journey I have started. I decided to challenge myself and gradually develop this library into a real one! Accordingly, I will write up the process of design and implementation of […]
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I’m planning to do a research project which needs reading an analog sensor using Raspberry Pi Pico W and feed it to a TinyML model, store the results and provide a RESTFUL api for data transfer. To have a faster development process, I have decided to give MicroPython a try for this project. There would […]
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During the past few weeks I was working on a project to detect an object from two or more cameras mounted in different perspectives. The initial intuitive idea is to save images with a timestamp and then match the images with a time interval of less than a defined threshold. Accordingly, I have written a […]
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Implementing a real-life example of things that I’m trying to comprehend, is the most exciting part of the learning process for me. It gives me a chance to encounter with dark corners and pitfalls. Accordingly, I’m going to briefly write up about a recent project I have done on simple audio classification using machine learning. […]
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I have decided to start a series of blog posts to build common machine learning algorithms from scratch inorder to clarify these methods for myself and make sure I deeply understand the mechanics behind each of them.
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Both PyTorch and TensorFlow can utilize GPUs to train neural networks faster. To make them work, it’s important to setup the whole mechanics properly. That’s why I decided to write about the steps, tricks, and solutions for the issues I encountered.