How to Transform and Improve Your Deep Learning Code to a Visual Neural Network

Dive into a comprehensive 42-minute tutorial that demystifies neural networks through visualization techniques. Learn how to transform your deep learning code into visual representations, making complex concepts easier to understand and explain. Begin with an introduction to TensorFlow Playground and fundamental concepts before delving into practical applications. Master data preparation for deep learning, create neural networks, map network layers, and compile models with specific parameters. Explore network visualization tools like Netron and Net2Vis to gain deeper insights into your models. Discover how to modify neural networks, export Colab notebooks to GitHub, and effectively save models to disk. This hands-on guide equips you with the skills to not only comprehend neural networks but also to articulate their intricacies to others, enhancing your ability to work with and explain deep learning concepts.

- Tutorial Starts
- TensorFlow Playground
- Other Tutorials to get you started with fundamentals
- Understanding the problem
- Tutorial Starts
- Data Preparation for Deep Learning
- Creating Deep Learning Neural Network
- Network mapping with layers
- Model compilation with parameters
- Saving model to disk
- Network Visualization with Netron
- Neural Network modification
- Network Visualization with Net2Vis
- Colab Notebook export to GitHub
- Recap