Sure. I'm assuming that you are going with Windows and Python for an easier start, and here are the steps beginning with what to install:

1. Set up your python environment. Pytorch is a library of Python, which means you will need a Python environment to install and manage the packages, as well as edit and run your code. If you want to start faster, you can try **Google Colab**, which is a free web IDE with pre-built python libraries. It allows you to jump directly to step 4, but it uses your Google Drive for storage, which limits the size of your datasets and sometimes causes compatibility issues. you can also use a local Python IDE such as **Pycharm** and **Visual Studio**. Another option is setting up your own python environment, which is harder but offers more flexibility. An easier start is to use **anaconda** as the environment manager and **Visual Studio Code** for code editing.

2. Install CUDA. CUDA allows Pytorch to use the power of your GPU, which is usually much faster. Check https://pytorch.org/ for the supported version of CUDA if you have an Nvidia GPU, but you can also begin with CPU to avoid possible compatibility problems if you are not going to work on large neural networks or datasets.

3. Install Pytorch. Also, check https://pytorch.org/ for the prerequisites for other Python libraries such as **numpy**, and installation commands of Pytorch for different versions. They can be run by your Python environment's package installer, and IDEs should also have the tools to run these commands. Note that some IDEs will autofill part of the command, so check for repeats after pasting the command.

4. Try machine learning. You can find tutorials at https://pytorch.org/tutorials/ on creating neural network models, preparing datasets, training, testing, etc. There are also detailed descriptions if you want to dive deeper.
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