Author: Fritz

The ability of a machine or program to identify spoken words and transcribe them to readable text is called speech recognition (speech-to-text). In this tutorial, I will be walking you through analyzing speech data and converting them to a useful text for sentiment analysis using Pydub and SpeechRecognition library in Python.

Sentiment analysis is the use of natural language to classify the opinion of people. It helps to classify words (written or spoken) into positive, negative, or neutral depending on the use case. The sentiment analyzed can help identify the pattern of a product; it helps to know what the users are saying and take the necessary steps to mitigate any problems.

Continue reading Sentiment Analysis of Speech Using PyDub and SpeechRecognition in Python

If our data isn’t good enough, there’s no machine learning tool, platform, or framework that exists that will work well—no matter how good the algorithm is.

So while debugging machine learning models, we also need to make sure our input data is prepared properly. For example the input data may not be a valid data type for a particular feature. Like in case of gender the allowed values are M or F, while the input data may contain other letter values for this feature.

Continue reading Debugging machine learning models

For years now, Snapchat has been at the forefront of mobile machine learning — their popular Lenses, which often combine on-device ML models with augmented reality, have become shining examples of the power and flexibility of on-device machine learning.

Given our respect and admiration for Snap’s work in this area, our team was thrilled to hear about the recent release of SnapML, Snap’s new ML framework inside their development platform Lens Studio (released with 3.0).

Continue reading Exploring SnapML: A Technical Overview

Last year, at I/O 2018, Google announced a brand new SDK available for developers: ML Kit. It’s no surprise that Google’s advances in machine learning are miles ahead of what any other company is aiming for. Through this SDK, Google was hoping to help mobile developers bring machine learning to their apps with simple, concise code. As part of the Firebase ecosystem, ML Kit allows developers to implement ML functionality with just a few lines of code; everything from vision to natural language to custom models.

Continue reading Exploring the new ML Kit features on iOS using Swift

One thing I’ve observed in many data science tutorials when it comes to modeling, is that once a certain performance threshold is achieved on test data, rarely is the model deployed/pushed to production—and it’s a common case in the industry more broadly.

This tutorial aims to take modeling a step further by building a REST API and deploying the model into production. In addition to the REST API, we’re building a simple web application that predicts whether a piece of text belongs to any of these classes: atheism, computer graphics, medical science, Christianity, or politics.

Continue reading Deploying and Hosting a Machine Learning Model Using Flask, Heroku and Gunicorn

You must’ve heard about “Vin Rouge” by @Nikhilodeon12 by now. The newly-verified Snap Star Nikhil created this lens, sparking the viral “Silhouette” challenge on TikTok. That’s right, TikTok.

The platform filled with its own filters, special effects, and AR tools. How did a Snapchat Lens end up over there?

Continue reading “How Snapchat Lenses affect TikTok trends — and why Lens Creators are so important”

In my article on lessons from my first deep learning hackathon, I introduced heatmaps. I mentioned that they highlight the regions in an image that the CNN focuses on while trying to make a prediction, and that it would be interesting to learn how they’re generated. Well, I finally know how. Let’s first go back and see what they look like.

Continue reading Heatmaps and CNNs Using Fast.ai

In this post, we’re going to investigate the field of image compression and it’s applications in real world. We’ll explore various machine and deep learning techniques for image compression and inspect their pros and cons, and their practical feasibility in real-world scenarios.

So let’s get started!

Image compression refer to reducing the dimensions, pixels, or color components of an image so as to reduce the cost of storing or performing operations on them. Some image compression techniques also identify the most significant components of an image and discard the rest, resulting in data compression as well.

Continue reading Image Compression Using Different Machine Learning Techniques

Google Colab is a cloud-based service that allows the execution of Python code and includes the ability to use and install new libraries. Put simply, you can leverage Google Colab to create a Jupyter Notebook and execute Python code using either a CPU or a free GPU.

Continue reading Getting Started with Google Colab Notebooks