Getting Started with Automated Data Pipelines, Day 2: Validation and URL...

• Data validation creating data from URL
• When do you need data from URL? Maps, getting shapes for maps
• 
  

Kaggle Challenge (LIVE)

• Architecture: UNet
• Use Google Colab to avoid dependent
• Salt correlated with oil and gas where salt is heavy
• !pip install imageio
• for image processing
• !pip install torch
• 
  

Kaggle Live-Coding: Code Reviews! | Kaggle

• Make code robust and reproducible, if column names change later can you still handle it. 
• Use R functions for column querying starts_with(), ends_with(), contains() makes the query more robust, harder to break downstream. 
• Avoid using numeric column indexing as order of columns may change
• Avoid redundancy in code and comments
• If want to make file a bit shorter, can avoid inline images, use script to generate images instead. 
• Make sure the logic matches the coding comment and function signature

Django Girls - friendly events that teach women build websites using Django

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Two amazing ladies from Poland teamed up with coaches around the world to teach girls and women how to use Web Development Framework Django

Machine Learning with No Code

AutoML machine learning deep learning without code by Uber, Ludwig allows users to train and make inference deep learning model without coding (caveat you still have to use command line code). Previously, it is an internal tool at Uber now open sourced to gather contribution. It's a python library.



Sraj Raval gives this tutorial using Ludwig in Google Colab.

Sraj Raval expression quote, "Don't hate. Copy & Paste." To install Ludwig copy and paste installation code from Uber github page.

Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with...

Matrix is a rank 2 tensor. There are two axis one is an array, one is individual numbers.
Check the dimensions of tensors using .size() or .shape()
Obtain the rank of the tensor by checking the length of its shape
len(tensor.shape) #returns 2 for matrix
number of elements in the tensor, is the product of the component values in the shape torch.tensor(my_tensor.shape).prod()
my_tensor.numel() #number of elements
number of elements is important in reshaping
reshaping does not change underlining data just change the shape

Kaggle Challenge (LIVE)

Taxi Duration Challenge by Sraj Raval.