jovian assignment 1 answers

JovianML - ZerotoGAN - Assignment 1

Some useful methods from pytorch tensor class.

Here I will be discussing some of the useful pytorch methods that will be useful for your datascience projects.

• torch.arange

torch.tensor() always copies data. If you have a Tensor data and just want to change its requires_grad flag, use requires_grad_() or detach() to avoid a copy. If you have a numpy array and want to avoid a copy, use torch.as_tensor().

Function 1 - torch.add

Add a scalar or tensor to self tensor. If both alpha and other are specified, each element of other is scaled by alpha before being used.

When other is a tensor, the shape of other must be broadcastable with the shape of the underlying tensor

The first example adds a one dimensional tensor and returns the answer as a one dimensional tensor

The second example adds a one dimensional four elements to another one dimensional four elements tensor and returns the answer as a four element tensor.

The third example when the dimension of the operating tensor vary, they throw a runtime error

Function 2 - torch.cat

Concatenates the given sequence of seq tensors in the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be empty.

The first example concatenates the elements of the tensor by appending to the vertical dimension

The second example concatenates the elements of the tensor by appending to the horizontal dimension

The third example breaks when applied to tensors of varying sizes.

Function 3 - apply_(callable) → Tensor

Applies the function callable to each element in the tensor, replacing each element with the value returned by callable.

The first example applies the square function to the elements of the tensor

The second example applies the cube function to the tensor at hand

The third example breaks when applied to the tensor of devicetype gpu, because of its incompatibility.

Function 4 - torch.abs

Computes the element-wise absolute value of the given input tensor.

The first example applies the abs function to a scalar tensor

The second example applies the abs function to a one dimensional tensor

The third example breaks when abs is applied to boolean.

Function 5 - torch.arange

The first example gets a range of values without step specified

The second example gets a range of values with step specified

The third example breaks when step value is set to zero

Here I have randomly picked some methods from pytorch, pytorch is really vast and it is still growing. Hope you like the methods that I have taken here to be explained. Thank you!

Reference Links

• Documentation for torch.Tensor : https://pytorch.org/docs/stable/tensors.html

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Assignment 3 - Pandas Data Analysis Practice

This assignment is a part of the course "Data Analysis with Python: Zero to Pandas"

In this assignment, you'll get to practice some of the concepts and skills covered this tutorial: https://jovian.ml/aakashns/python-pandas-data-analysis

As you go through this notebook, you will find a ??? in certain places. To complete this assignment, you must replace all the ??? with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.

Some things to keep in mind:

• Make sure to run all the code cells, otherwise you may get errors like NameError for undefined variables.
• Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.
• In some cases, you may need to add some code cells or new statements before or after the line of code containing the ??? .
• Since you'll be using a temporary online service for code execution, save your work by running jovian.commit at regular intervals.
• Questions marked (Optional) will not be considered for evaluation, and can be skipped. They are for your learning.

You can make submissions on this page: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-3-pandas-practice

If you are stuck, you can ask for help on the community forum: https://jovian.ml/forum/t/assignment-3-pandas-practice/11225/3 . You can get help with errors or ask for hints, describe your approach in simple words, link to documentation, but please don't ask for or share the full working answer code on the forum.

How to run the code and save your work

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org , a free online service for running Jupyter notebooks.

Before staring the assignment, let's save a snapshot of the assignment to your Jovian.ml profile, so that you can access it later, and continue your work.

Assignment 1 - Binary Search Practice

This assignment is a part of the course "Data Structures and Algorithms in Python" .

In this assignment, you'll get to practice some of the concepts and skills covered in the following notebooks:

• Binary Search and Complexity Analysis
• Solving Programming Problems Systematically

As you go through this notebook, you will find a ??? in certain places. To complete this assignment, you must replace all the ??? with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.

Some things to keep in mind:

• Make sure to run all the code cells, otherwise you may get errors like NameError for undefined variables.
• Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.
• In some cases, you may need to add some code cells or new statements before or after the line of code containing the ??? .
• Since you'll be using a temporary online service for code execution, save your work by running jovian.commit at regular intervals.
• Questions marked (Optional) will not be considered for evaluation, and can be skipped. They are for your learning.

You can make submissions on this page: https://jovian.ai/learn/data-structures-and-algorithms-in-python/assignment/assignment-1-binary-search-practice

If you are stuck, you can ask for help on the community forum: https://jovian.ai/forum/c/data-structures-and-algorithms-in-python/assignment-1/87 . You can get help with errors or ask for hints, but please don't ask for OR share the full working answer code on the forum.

How to run the code and save your work

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org , a free online service for running Jupyter notebooks.

This tutorial is an executable Jupyter notebook . You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer .

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder . You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.

Option 2: Running on your computer locally

To run the code on your computer locally, you'll need to set up Python , download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.

Saving your work

Before staring the assignment, let's save a snapshot of the assignment to your Jovian profile, so that you can access it later, and continue your work.