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Pedestrian Detection using PyTorch

July 9, 2021July 18, 2021 Shashank Shekhar Computer Vision

In this blog post, you will learn how to implement a Pedestrian Detection algorithm using PyTorch.

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Mastering Pandas

July 18, 2021 Kevin Jacobs

In this course, you will learn how to use the Python Pandas. After the course, you will be able to:

Load and transform your data
Visualizing data using line plots, scatter plots and histograms
Merging and storing data

The course also includes more advanced topics, such as data parallelization and aggregation.

You can see all course content under “Curriculum” on Data Blogger Courses and the first three lessons are free. The first free lesson can be found here.

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Finetuning BERT on Google Colab

July 7, 2021July 18, 2021 Kevin Jacobs Natural Language Processing

In this blog post, you will learn how you can finetune BERT on Google Colab and use the trained model for your NLP task. You will also learn how you can observe the performance of the model in the training phase.

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Simplify your PyTorch model using PyTorch Lightning

June 11, 2021July 18, 2021 Kevin Jacobs Coding

Code for machine learning can get repetitive. In this blog post, you will learn to combat code repetition by using PyTorch Lightning.

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Proof that 1/7 is a repeated decimal

October 5, 2018October 5, 2018 Kevin Jacobs Mathematics

This blog post serves as an exercise and solution to the following question:

$\frac{1}{7}=0.\overline{142857}$

In plain English: is the fraction $\frac{1}{7}$ a repeated decimal (0.142857142857142857…)?

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Scale out your Pandas DataFrame operations using Dask

August 5, 2018July 18, 2021 Kevin Jacobs Coding

In Pandas, one can easily apply operations on all the data using the apply method (see also our course for learning Pandas quickly). However, this method is quite slow and is not useful when scaling up your methods. Is there a way to speed up these operations? And if so, how? Yes, there is! This blog post will explain how you can use Dask to maximize the power of parallelization and to scale out your DataFrame operations.

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