This is the repository for D-Lab’s six-hour Introduction to Deep Learning in Python workshop. View the associated slides here.
Convey the basics of deep learning in Python using keras on image datasets. Students are empowered with a general grasp of deep learning, example code that they can modify, a working computational environment, and resources for further study.
- Installation
- Jupyter Notbook
- Keras and Tensorflow
- Helper packages
- What is “deep” learning?
- Understanding the dataset
- Dataset splitting: training, test, cross-validation
- Defining moving parts of a deep learning model
- Understanding a loss function, activation function, and metrics
- Performance evaluation
- Part 1
- MNIST 0-9 hand-written digit example
- Feed Forward (Vanilla) Neural Networks
- Part 2
- CIFAR10 - 10 image type classification
- Convolutional Neural Networks
This is an advanced level workshop. Participants should be intermediate Python users and have had some prior exposure to machine learning.
We assume the following background:
- D-Lab's Python Machine Learning Fundamentals (6 hours)
- Or, comparable experience/training, assuming familiarity with:
- Basic Python syntax
- Train/validation/test splitting
- Dataset cleaning
- Overfitting / underfitting / generalization
- Hyperparameter customization
- Basic linear algebra (vector, matrix, etc.)
- Basic statistics (linear regression)
If you are not comfortable installing packages, writing your own Python code, and using Jupyter Notebooks, this will not be a good workshop for you.
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Datahub - If you are a UC Berkeley student, use D-Lab's Datahub (highly recommended), click this link:
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Google Colab - If you are not a UC Berkeley student, use the following links to open up each Jupyter Notebook in a Google Colab session:
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If you would like to install packages locally on your computer, see the installation instructions.
- This process can take about 30-60 minutes, so be sure to try and do this before class!
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Massive open online courses
- fast.ai - Practical Deep Learning for Coders
- Kaggle Deep Learning
- Google Machine Learning Crash Course
- See this sweet interactive learning rate tool
- Google seedbank examples
- DeepLearning.ai
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Workshops
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Stanford
- CS 20 - Tensorflow for Deep Learning Research
- CS 230 - Deep Learning
- CS 231n - Neural Networks for Visual Recognition
- CS 224n - Natural Language Processing with Deep Learning
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Berkeley
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UToronto CSC 321 - Intro to Deep Learning
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Books
- F. Chollet and J.J. Allaire - Deep Learning with Python
- Charniak E - Introduction to Deep Learning
- I. Goodfellow, Y. Bengio, A. Courville - www.deeplearningbook.org
- Zhang et al. - Dive into Deep Learning
- Sean Perez
- Pratik Sachdeva