Welcome to your journey into the fascinating world of Creative Applications of Deep Learning! This blog will guide you on effectively utilizing the resources provided by the course for hands-on learning, especially with TensorFlow.

Understanding Your Course Structure

The course is divided into three levels, each containing a series of sessions designed to build your knowledge step by step. Think of it like assembling a complex puzzle where each course is a different piece that connects to complete the picture of deep learning. Here’s how the courses are structured:

• Course 1: Creative Applications of Deep Learning with TensorFlow I
  • Session 1: Introduction to TensorFlow
  • Session 2: Training a Network with TensorFlow
  • Session 3: Unsupervised and Supervised Learning
  • Session 4: Visualizing and Hallucinating Representations
  • Session 5: Generative Models
• Course 2: Creative Applications of Deep Learning with TensorFlow II
  • Session 1: Cloud Computing, GPUs, Deploying
  • Session 2: Mixture Density Networks
  • Session 3: Modeling Attention with RNNs
  • Session 4: Image-to-Image Translation with GANs
• Course 3: Creative Applications of Deep Learning with TensorFlow III
  • Session 1: Modeling Music and Art
  • Session 2: Modeling Language: Natural Language Processing
  • Session 3: Autoregressive Image Modeling with PixelCNN
  • Session 4: Modeling Audio with Wavenet and NSynth

Setting Up Your Environment

To get started, you’ll need to set up Jupyter Notebook and necessary Python libraries. You have two options for installation:

Method 1: Using pip Install

If you have Python installed and you’re comfortable using it, simply run:

pip install tensorflow

Or for GPU support:

pip install tensorflow-gpu

Method 2: Docker Installation

If you prefer a controlled environment, use Docker. Follow these steps:

git clone --recursive https://github.com/pkmital/CADL.git
cd CADL
docker build -t cadl .

Then run:

docker run -it -p 8888:8888 -p 6006:6006 -v $(pwd)/session-1:notebooks --name tf cadl bash

This will get you started with a fully functional environment for the course.

Navigating Jupyter Notebook

Once you have Jupyter running, navigate to http://localhost:8888 in your web browser. You will find links to your homework assignments and lecture transcripts. It’s like your own personal library of deep learning resources!

Troubleshooting Common Issues

Sometimes, things may not work as you expect. Here’s a quick guide to common issues and how to resolve them:

1. ImportError: No module named tensorflow

Ensure you’re using the right version of Python. Check your installations using:

which python3
python3 --version

2. Kernel is always busy

Try using a different web browser or check Docker settings for any firewall issues as described in the Docker Toolbox section.

3. Unable to interact with the notebook

Ensure you are accessing the correct URL based on your Docker setup or locally installed Jupyter.

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Conclusion

As you embark on this learning adventure, remember that practice is key. Engage with the materials provided, and don’t hesitate to experiment with your own ideas. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.