Author: Fritz

Deep learning has seen a lot of progress in recent years. It’s hard to think of an industry that doesn’t use deep learning. The availability of large amounts of data along with increased computation resources have fueled this progress. There have been many well known and novel methods responsible for the growth of deep learning.

One of those is transfer learning, which is the method of using the representations/information learned by one trained model for another model that needs to be trained on different data and for a similar/different task. Transfer learning uses pre-trained models (i.e. models already trained on some larger benchmark datasets like ImageNet).

Continue reading Pre-Trained Machine Learning Models vs Models Trained from Scratch

Text summarization is a well-known task in natural language processing. In general, summarization refers to presenting data in a concise form, focusing on parts that convey facts and information, while preserving the meaning. To understand this better, we can think of summarization as a process to find a subset of data that contains all the information.

Continue reading Extractive Text Summarization Using Neural Networks

Living in a “big” city like Casablanca, you tend to forget how the air is polluted — and somehow get used to it.

But don’t get fooled—in addition to emissions from vehicles, the air breathed by citizens in most big cities is contaminated by significant atmospheric emissions from factories and other sources of pollution.

Continue reading “Build an iOS Application to Predict Air Pollution Using a Random Forest Regressor”

On-device machine learning is gaining more and more traction among developers around the world. It’s now considered the best way to bring machine-learning powered tools and applications to the masses. The reason behind this is the ever-increasing on-device computational power that can be harnessed by developers, as well as the increased data privacy that on-device machine learning offers.

Continue reading Machine Learning on iOS 12 and the New iPhone X Series

There has been tremendous development and progress over the past few years in how some technologies have impacted and blended into our human lives.

Deep learning has been at the forefront of such technologies and has found applications in many industries — retail, health, automobiles, finance, and transportation to name a few.

Continue reading “Model Interpretability and Visualization: Looking Inside the Neural Network Black Box”

Neural Network (NN) Pruning is a task of reducing the size of a Neural Network by removing some of its parameters/weights.

Pruning is often performed with the objective of reducing the memory, computational, and energy bandwidths required for training and deploying NN models which are notorious for their large model size, computational expense, and energy consumption.

Continue reading Neural Network Pruning Research Review 2020

In this post, we’re going to investigate the field of sign language recognition and its applications in the real world.

We are going to discuss the approaches adopted by a research paper on Indian Sign Language Recognition and try to understand the merits and demerits of these methods from a practical point of view.

Continue reading “Exploring Sign Language Recognition techniques with Machine Learning”

Feature selection is an important task for any machine learning application. This is especially crucial when the data in question has many features. The optimal number of features also leads to improved model accuracy. Obtaining the most important features and the number of optimal features can be obtained via feature importance or feature ranking. In this piece, we’ll explore feature ranking.

Continue reading Feature Ranking with Recursive Feature Elimination in Scikit-Learn

After working on a couple of projects using handwritten text recognition, I’m in total awe of this technology: Send an image to a REST endpoint, wait for the machine learning magic to happen, and then receive a bunch of JSON data with your recognized text as the response /output—wow!

But I already “awed” a lot about this in a blog post a while back. For anyone who’s interested in it and doesn’t know how to get started — check this out:

Continue reading Intro to machine learning on iOS: Using Core ML to recognize handwritten digits