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Julia files inspired by Google's Machine Learning Crash Course
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1. First steps with Tensorflow Julia.ipynb
10. Multi-class Classification of Handwritten Digits Julia.ipynb
11. Intro to Sparse Data and Embeddings Julia.ipynb
2. Synthetic Features and Outliers Julia.ipynb
3. Validation Julia.ipynb
4. Feature Sets Julia.ipynb
5. Feature Crosses Julia.ipynb
6. Logistic Regression Julia.ipynb
8. Intro to Neural Nets Julia.ipynb
9. Improving Neural Net Performance Julia.ipynb
Conversion of Movie-review data to one-hot encoding.ipynb
LICENSE
MNIST.jl
README.md
TFrecord Extraction.ipynb

README.md

MLCrashCourse

Julia files inspired by Google's Machine Learning Crash Course

The files in this repository reproduce most of the programming exercises in Google's Machine Learning Crash Course (https://developers.google.com/machine-learning/crash-course/) using Tensorflow.jl (https://github.com/malmaud/TensorFlow.jl). An accompanying blog can be found at https://tensorflowjulia.blogspot.com.

All files have been updated to work on Julia 1.1. At the time of writing (Mar 2019), this requires the developmental versions of the PyCall.jl and Tensorflow.jlpackages.

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