Apple’s M1 Pro and M1 Max Outperform Google Colab by up to 54%
The main keyboard I used is attached to a MacBook Pro. I’m typing this on a MacBook Pro. And being the tech nerd I am, when Apple released a couple of new MacBook Pro’s with upgraded hardware: M1 Pro, M1 Max chips and redesigns and all the rest, I decided, I better test them out. For context, I make videos on machine learning, write machine learning code and teach machine learning. That’s what thi
Checkbox/Table cell detection using OpenCV-Python
Source: Image by AuthorIntroductionIn this age of Digital Transformation, Information Extraction is one of the key areas of Business interest, where we need to extract relevant information from unstructured data sources like scanned invoices, bills, etc into structured data, using Computer Vision and Natural Language Processing. Here, the primary steps we depend on are Optical Character…
Beyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head
WhyWhen confronting a new data science problem, one of the first questions to ask is which technology to use. There is hype; there are standard tools; there are bleeding-edge technologies, entire platforms and off-the-shelf solutions. Over the last few years, I’ve been building proof of concepts and solutions with any technology I could get my hands on. If there is a new platform, I register for t
ML Ops: Machine Learning as an Engineering Discipline
So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved
FGO StyleGAN: This Heroic Spirit Doesn’t Exist
When I first saw Nvidia’s StyleGAN’s results I felt like it looked like a bunch of black magic. I am not as experienced in the area of GANs as other parts of deep learning, this lack of experience and the thought that I really lacked the GPU firepower to train up my own StyleGAN stopped me from jumping in sooner. For scale, on the StyleGAN github Nvidia lists the GPU specifications, basically sayi
Lit BERT: NLP Transfer Learning In 3 Steps
BERT (Devlin, et al, 2018) is perhaps the most popular NLP approach to transfer learning. The implementation by Huggingface offers a lot of nice features and abstracts away details behind a beautiful API. PyTorch Lightning is a lightweight framework (really more like refactoring your PyTorch code) which allows anyone using PyTorch such as students, researchers and production teams, to scale deep l
My secret sauce to be in top 2% of a kaggle competition
Competing in kaggle competitions is fun and addictive! And over the last couple of years, I developed some standard ways to explore features and build better machine learning models. These simple, but powerful techniques helped me get a top 2% rank in Instacart Market Basket Analysis competition and I use them outside of kaggle as well. So, let’s get right into it! One of the most important aspect
Anchor Boxes — The key to quality object detection
One of the hardest concepts to grasp when learning about Convolutional Neural Networks for object detection is the idea of anchor boxes. It is also one of the most important parameters you can tune for improved performance on your dataset. In fact, if anchor boxes are not tuned correctly, your neural network will never even know that certain small, large or irregular objects exist and will never h
Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters
The year 2018 marked a turning point for the field of Natural Language Processing, with a series of deep-learning models achieving state-of-the-art results on NLP tasks ranging from question answering to sentiment classification. Most recently, Google’s BERT algorithm has emerged as a sort of “one model to rule them all,” based on its superior performance over a wide variety of tasks. 🕹 Try out a
A first look at Pytorch 1.0
Image by FacebookI had the pleasure of attending the first Pytorch Developer Conference this Oct 2nd. And it was a great experience! Pytorch has quickly become a first-class citizen among deep learning frameworks, having started out just two years ago. There were a lot of familiar faces with whom I caught up over the event. I’ll be covering those parts of the Pytorch 1.0 morning session that were
Deep Learning Framework Power Scores 2018
Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Then I w
Use Kaggle to start (and guide) your ML/ Data Science journey — Why and How
https://www.kaggle.com/I often get asked by my friends and college-mates — “How to start Machine Learning or Data Science”. Earlier, I wasn’t so sure. I would say something like do this course or read this tutorial or learn Python first (just the things that I did). But now, as I am going deeper and deeper into the field, I am beginning to realise the drawbacks of the approach that I took. So, in
A Practitioner's Guide to Natural Language Processing (Part I) — Processing & Understanding Text
IntroductionUnstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all
Audio processing in TensorFlow
An implementation of the Short Time Fourier TransformI found audio processing in TensorFlow hard, here is my fixThere are countless ways to perform audio processing. The usual flow for running experiments with Artificial Neural Networks in TensorFlow with audio inputs is to first preprocess the audio, then feed it to the Neural Net. What happens though when one wants to perform audio processing so
Detecting bats by recognising their sound with Tensorflow
Last week I discovered that there are bats behind my appartment. I immediately grabbed my “bat detector”: a device that converts the ultrasound signals bats use to echolocate from an inaudible frequency range to an audible one. The name “bat detector” thus is a lie: you can use it to detect bats, but it does not detect bats itself. In this tutorial I will show you how to build a real bat detector
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