spidr
mit-deep-learning-book-pdf
| spidr | mit-deep-learning-book-pdf | |
|---|---|---|
| 4 | 3 | |
| 61 | 12,323 | |
| - | - | |
| 5.9 | 2.7 | |
| 7 days ago | 7 months ago | |
| Idris | Java | |
| Apache License 2.0 | - |
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spidr
- Accelerated machine learning with dependent types
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[Project] Idris and XLA: linear algebra and probabilistic modelling w. dependent types
In June, I announced I'd started work on a probabilistic modelling library in Idris. This post is to announce the first major milestone: basic linear algebra in Idris backed by XLA. Right now, this is only addition, but other ops are easy to add from here.
[In June](https://www.reddit.com/r/MachineLearning/comments/o9lqb8/probabilistic_modelling_project_w_dependent_types/?utm_source=share&utm_medium=web2x&context=3), I started work on a probabilistic modelling [library](https://github.com/joelberkeley/spidr) in Idris. This post is to announce the first major milestone: *basic linear algebra in Idris backed by XLA*. Right now, this is just addition, but adding ops will be easy from here.
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ML engineering as [research]. Probabilistic modelling [project] w. dependent types. Early stages
I've been working almost full time on a probabilistic modelling framework with an API in Idris, a purely functional programming language with a very advanced type system incl. dependent types (you can parametrise tensor types by their shape, and more), quantitative types and theorem proving.
mit-deep-learning-book-pdf
- Deep Learning Course
- Is supervised machine learning the same as linear regression?
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NLP resources
I remember an NLP course on DataCamp being helpful as an intro, but a resource I keep handy is Hands-On Machine Learning (Geron) which has really helpful follow along notebooks on the git. Then when you want some background: Deep Learning (Goodfellow)
What are some alternatives?
matrix-sized - Generic matrix with statically known size and bindings to C++ linear algebra libraries (Eigen, Spectra).
handson-ml2 - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Idris2 - A purely functional programming language with first class types
jblas - Linear Algebra for Java
100-Days-Of-ML-Code - 100 Days of ML Coding
Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
pyro - Deep universal probabilistic programming with Python and PyTorch
jcohere - jCohere is a java client for accessing the Cohere.ai platform
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
zero_to_gpt - Go from no deep learning knowledge to implementing GPT.