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With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just a few lines of code. Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including GPUs and TPUs, regardless of the power of your machine.
access Jupyter Notebooks online, specifically using Google Colab. run python code in Jupyter Notebooks; 1. Introduction to Jupyter Notebooks. Jupyter Notebooks are a fantastic tool for writing and running Python code. We'll use Google Colab as an online Jupyter Notebook environment. 2. Basic Layout of Jupyter Notebooks 2.1 Markdown Cells:
This notebook simulates portfolio returns using Monte Carlo methods to estimate Value at Risk (VaR) and Conditional Value at Risk (CVaR). It begins by importing historical price data for various sectors, calculating daily returns, and determining portfolio statistics.
How to use R in Google Colab. There are two ways to run R in Colab. The first way is to use the rpy2 package in the Python runtime. This method allows you to execute R and Python syntax together. The second way is to actually start the notebook in the R runtime.
The logging module in Python is a ready-to-use and powerful module that is designed to meet the needs of beginners as well as enterprise teams. It is used by most of the third-party Python libraries, so you can integrate your log messages with the ones from those libraries to produce a homogeneous log for your application.
To summarize this section, we first introduced our primary dataset we will be using for the next few sections. Then, we ran our first query by first installing JupySQL and other packages into our notebook, properly loaded our data with some Python, and established a connection to a DuckDB database.
Google Colab is an amazing tool that lets us build and execute an outstanding data science model and provides us with an opportunity to document our journey. As Google Colab provides us code cells to type the code, it also provides us with text cells to add the text. In this tutorial, we will focus more on the text cell and see how we can ...
assert(X_val.shape[0] == y_val.shape[0]), "The number of images is not equal to the number of labels". assert(X_test.shape[0] == y_test.shape[0]), "The number of images is not equal to the number of labels". assert(X_train.shape[1:] == (32, 32, 3)), "The dimensions of the image is not 32*32*3".
Welcome to this hands-on training where you will beging to learn how to work with text data in Python! In this session you will learn: How to explore and visualize your text data. How to manipulate and clean text data for further analysis. The basics of regex, and how to use it to filter a DataFrame.
Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub.
