Are you ready to dive into the exciting world of deep learning? The NYU Deep Learning Spring 2020 course provides a comprehensive set of resources to guide you through this journey. This article will take you step-by-step on how to set everything up so you can confidently engage with the course material.

Prerequisites

To get started, ensure you have a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. Users of Mac or Ubuntu Linux will find the setup straightforward, while Windows users will need to use the Git BASH terminal.

Step 1: Download and Install Miniconda

Follow these simple steps to install Miniconda:

• Visit the Anaconda website.
• Download the latest version of Miniconda for Python 3.7 that is compatible with your operating system.
• Open your terminal and execute the following commands:

bash
wget http://link_to_miniconda.sh

Step 2: Check Out the Git Repository

Once Miniconda is installed, proceed to checkout the course repository:

bash
git clone https://github.com/Atcold/NYU-DLSP20.git

Step 3: Create an Isolated Miniconda Environment

Next, create a dedicated environment for the course:

bash
# Change directory into the course folder
cd NYU-DLSP20

# Create the environment
conda env create -f environment.yml

# Activate the environment
source activate NYU-DL

Step 4: Start Jupyter Notebook or JupyterLab

After setting up your environment, you’ll want to engage with the interactive data exploration tools provided:

• To start JupyterLab, type:

bash
jupyter lab

• For the classic Jupyter Notebook interface, use:

bash
jupyter notebook

Notebooks Visualization

Jupyter Notebooks are a core part of the lectures, providing interactive data exploration and visualization. It’s recommended to use dark themes for both GitHub and Jupyter Notebooks for better aesthetics.

Installing Dark Themes

• To install the dark theme for Jupyter Notebook, visit this link.
• For the GitHub dark theme, refer to this page and remember to comment out the `invert #fff to #181818` code block to keep the layout looking fresh.

Troubleshooting Ideas

If you encounter issues during the installation or while running Jupyter, consider the following:

• Make sure that all commands are run in the terminal without any typo.
• Verify that Miniconda is installed correctly by typing conda --version in the terminal.
• If Jupyter doesn’t start, ensure the environment is activated by using conda activate NYU-DL.
• For more insights, updates, or to collaborate on AI development projects, stay connected with [fxis.ai](https://fxis.ai/edu).

Conclusion

That’s it! You’re now equipped to explore the NYU Deep Learning course materials. With your Jupyter environment set up, you can start experimenting with deep learning right away.

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