Photo by Debashis RC Biswas on Unsplash

Start a new Notebook and add to it only the code needed to create and show the widgets that you need, and Markdown for any text that you want to appear.

Next install Voila

  • The first line installs the voila library and application, and
  • The Second line connects it to your existing jupyter notebook

To view your notebook as a Voila web application

Replace the word “notebooks” in your browsers URL with “voila/render”. You’ll see the same content as your notebook, but without any of the code cells.

Deploying to Binder <>

1. Add your notebook to a GitHub repository.
2. Paste the URL of that repo into…

In this article, we are going to take a look at How to deal with Custom PyTorch Dataset.

Photo by Joshua Earle on Unsplash

Custom datasets!! WHY??

Because you can shape it in a way you desire!!!

It is natural that we will develop our way of creating custom datasets while dealing with different Projects.

There are some official custom dataset examples on PyTorch Like here but it seemed a bit obscure to a beginner (like me, back then). The topics which we will discuss are as follows.

  1. Custom Dataset Fundamentals.
  2. Using Torchvision Transforms.
  3. Dealing with pandas (read_csv)
  4. Embedding Classes into File Names
  5. Using DataLoader

1. Custom Dataset Fundamentals.


The world’s simplest facial recognition API for Python and the command line

In this article, we’ll explore the ‘face_recognition’ library and implement a face recognition system.


  1. Step 1: Installation
  2. Step 2: Finding Faces in Image
  3. Step 3: Identifying Facial Features
  4. Step 4: Identifying Known Faces

Step 1: Installation:


  • Python 3.3+ or Python 2.7
  • macOS or Linux (Windows not officially supported, but it will work fine)

For windows, First make sure that you have dlib installed. If you don’t have dlib installed. Follow these steps

  • Clone the code from GitHub:
  • Install with Python Extension:

After this…

Mohammed Maheer

Proficient in Python. Passionate about Data Science, Machine Learning, Deep Learning.

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