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Resources for "Introduction to Deep Learning" course.
| Date | Stars |
|---|---|
| 2026-07-31 | 753 |
| 2026-08-06 | 753 |
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# Introduction to Deep Learning course resources https://www.coursera.org/learn/intro-to-deep-learning ## Running on Google Colab (tested for all weeks) Google has released its own flavour of Jupyter called Colab, which has free GPUs! Here's how you can use it: 1. Open https://colab.research.google.com, click **Sign in** in the upper right corner, use your Google credentials to sign in. 2. Click **GITHUB** tab, paste https://github.com/hse-aml/intro-to-dl and press Enter 3. Choose the notebook you want to open, e.g. week2/v2/mnist_with_keras.ipynb 4. Click **File -> Save a copy in Drive...** to save your progress in Google Drive 5. Click **Runtime -> Change runtime type** and select **GPU** in Hardware accelerator box 6. **Execute** the following code in the first cell that downloads dependencies (change for your week number): ```python ! shred -u setup_google_colab.py ! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py import setup_google_colab # please, uncomment the week you're working on # setup_google_colab.setup_week1() # setup_google_colab.setup_week2() # setup_google_colab.setup_week2_honor() # setup_google_colab.setup_week3() # setup_google_colab.setup_week4() # setup_google_colab.setup_week5() # setup_google_colab.setup_week6() ``` 7. If you run many notebooks on Colab, they can continue to eat up memory, you can kill them with `! pkill -9 python3` and check with `! nvidia-smi` that GPU memory is freed. **Known issues:** * Blinking animation with `IPython.display.clear_output()`. It's usable, but still looking for a workaround. ## Offline instructions Coursera Jupyter Environment can be slow if many learners use it heavily. Our tasks are compute-heavy and we recommend to run them on your hardware for optimal performance. You will need a computer with at least 4GB of RAM. There're two options to setup the Jupyter Notebooks locally: Docker container and Anaconda. ### Docker container option (best for Mac/Linux) Follow the instructions on https://hub.docker.com/r/zimovnov/coursera-aml-docker/ to install Docker container with all necessary software installed. After that you should see a Jupyter page in your browser. ### Anaconda option (best for Windows) We highly recommend to install docker environment, but if it's not an option, you can try to install the necessary python modules with Anaconda. First, install Anaconda with **Python 3.5+** from [here](https://www.anaconda.com/download). Download `conda_requirements.txt` from [here](https://github.com/ZEMUSHKA/coursera-aml-docker/blob/master/conda_requirements.txt). Open terminal on Mac/Linux or "Anaconda Prompt" in Start Menu on Windows and run: ``` conda config --append channels conda-forge conda config --append channels menpo conda install --yes --file conda_requirements.txt ``` To start Jupyter Notebooks run `jupyter notebook` on Mac/Linux or "Jupyter Notebook" in Start Menu on Windows. After that you should see a Jupyter page in your browser. ### Prepare resources inside Jupyter Notebooks (for local setups only) Click **New -> Terminal** and execute: `git clone https://github.com/hse-aml/intro-to-dl.git` On Windows you might want to install [Git](https://git-scm.com/download/win). You can also download all the resources as zip archive from GitHub page. Close the terminal and refresh Jupyter page, you will see **intro-to-dl** folder, go there, all the necessary notebooks are waiting for you. First you need to download necessary resources, to do that open `download_resources.ipynb` and run cells for Keras and your week. Now you can open a notebook for the corresponding week and work there just like in Coursera Jupyter Environment. ### Using GPU for offline setup (for advanced users) - If you have a **Linux host** you can try these instructions for Docker: https://github.com/ZEMUSHKA/coursera-aml-docker#using-gpu-in-your-container-linux-hosts-only - The easiest way is to go with A
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dbaa827c476d3403, topic:deep-learning