Course from Geekbrains University
About Geekbrains: We teach people to master programming, web design and marketing from the ground up. We conduct online courses with internships and free master classes, develop the community, cooperate with employment companies and continuously test new methods to improve the effectiveness of learning.
Introduction to mathematical analysis
- Linear space
- Matrices and matrix operations
- Linear transformations
- System of linear equations
- Singular Matrix Decomposition
Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with
- Zero configuration required
- Free access to GPUs
- Easy sharing
Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to learn more, or just get started below.
The document you are reading is not a static web page, but an interactive environment called a Colab notebook that lets you write and execute code.
To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut "Command/Ctrl+Enter". To edit the code, just click the cell and start editing.
Variables that you define in one cell can later be used in other cells.
Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see Overview of Colab. To create a new Colab notebook you can use the File menu above, or use the following link: create a new Colab notebook.
Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see jupyter.org.
- Overview of Colaboratory
- Guide to Markdown
- Importing libraries and installing dependencies
- Saving and loading notebooks in GitHub
- Interactive forms
- Interactive widgets
- TensorFlow 2 in Colab
- Loading data: Drive, Sheets, and Google Cloud Storage
- Charts: visualizing data
- Getting started with BigQuery
These are a few of the notebooks from Google's online Machine Learning course. See the full course website for more.
- Intro to Pandas
- Tensorflow concepts
- First steps with TensorFlow
- Intro to neural nets
- Intro to sparse data and embeddings
Pull requests are welcome.