Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Tensorflow | 172,427 | 327 | 77 | 11 hours ago | 46 | October 23, 2019 | 2,292 | apache-2.0 | C++ | |
An Open Source Machine Learning Framework for Everyone | ||||||||||
Transformers | 87,651 | 64 | 911 | 7 hours ago | 91 | June 21, 2022 | 615 | apache-2.0 | Python | |
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. | ||||||||||
Keras | 57,713 | 330 | a day ago | 68 | May 13, 2022 | 372 | apache-2.0 | Python | ||
Deep Learning for humans | ||||||||||
Tensorflow Examples | 42,312 | 5 months ago | 218 | other | Jupyter Notebook | |||||
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | ||||||||||
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AI-Powered Photos App for the Decentralized Web 🌈💎✨ | ||||||||||
Handson Ml | 24,975 | a day ago | 136 | apache-2.0 | Jupyter Notebook | |||||
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead. | ||||||||||
Ray | 24,732 | 80 | 199 | 8 hours ago | 76 | June 09, 2022 | 2,902 | apache-2.0 | Python | |
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. | ||||||||||
Handson Ml2 | 24,269 | 21 days ago | 193 | apache-2.0 | Jupyter Notebook | |||||
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. | ||||||||||
Data Science Ipython Notebooks | 23,924 | 5 months ago | 26 | other | Python | |||||
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||
Netron | 21,732 | 4 | 63 | 14 hours ago | 489 | July 04, 2022 | 22 | mit | JavaScript | |
Visualizer for neural network, deep learning, and machine learning models |
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.
TFP also works as "Tensor-friendly Probability" in pure JAX!:
from tensorflow_probability.substrates import jax as tfp
--
Learn more here.
Our probabilistic machine learning tools are structured as follows.
Layer 0: TensorFlow. Numerical operations. In particular, the LinearOperator
class enables matrix-free implementations that can exploit special structure
(diagonal, low-rank, etc.) for efficient computation. It is built and maintained
by the TensorFlow Probability team and is now part of
tf.linalg
in core TF.
Layer 1: Statistical Building Blocks
tfp.distributions
):
A large collection of probability
distributions and related statistics with batch and
broadcasting
semantics. See the
Distributions Tutorial.tfp.bijectors
):
Reversible and composable transformations of random variables. Bijectors
provide a rich class of transformed distributions, from classical examples
like the
log-normal distribution
to sophisticated deep learning models such as
masked autoregressive flows.Layer 2: Model Building
tfp.distributions.JointDistributionSequential
):
Joint distributions over one or more possibly-interdependent distributions.
For an introduction to modeling with TFP's JointDistribution
s, check out
this colab
tfp.layers
):
Neural network layers with uncertainty over the functions they represent,
extending TensorFlow Layers.Layer 3: Probabilistic Inference
tfp.mcmc
):
Algorithms for approximating integrals via sampling. Includes
Hamiltonian Monte Carlo,
random-walk Metropolis-Hastings, and the ability to build custom transition
kernels.tfp.vi
):
Algorithms for approximating integrals via optimization.tfp.optimizer
):
Stochastic optimization methods, extending TensorFlow Optimizers. Includes
Stochastic Gradient Langevin Dynamics.tfp.monte_carlo
):
Tools for computing Monte Carlo expectations.TensorFlow Probability is under active development. Interfaces may change at any time.
See tensorflow_probability/examples/
for end-to-end examples. It includes tutorial notebooks such as:
It also includes example scripts such as:
Representation learning with a latent code and variational inference.
For additional details on installing TensorFlow, guidance installing prerequisites, and (optionally) setting up virtual environments, see the TensorFlow installation guide.
To install the latest stable version, run the following:
# Notes:
# - The `--upgrade` flag ensures you'll get the latest version.
# - The `--user` flag ensures the packages are installed to your user directory
# rather than the system directory.
# - TensorFlow 2 packages require a pip >= 19.0
python -m pip install --upgrade --user pip
python -m pip install --upgrade --user tensorflow tensorflow_probability
For CPU-only usage (and a smaller install), install with tensorflow-cpu
.
To use a pre-2.0 version of TensorFlow, run:
python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9"
Note: Since TensorFlow is not included
as a dependency of the TensorFlow Probability package (in setup.py
), you must
explicitly install the TensorFlow package (tensorflow
or tensorflow-cpu
).
This allows us to maintain one package instead of separate packages for CPU and
GPU-enabled TensorFlow. See the
TFP release notes for more
details about dependencies between TensorFlow and TensorFlow Probability.
There are also nightly builds of TensorFlow Probability under the pip package
tfp-nightly
, which depends on one of tf-nightly
or tf-nightly-cpu
.
Nightly builds include newer features, but may be less stable than the
versioned releases. Both stable and nightly docs are available
here.
python -m pip install --upgrade --user tf-nightly tfp-nightly
You can also install from source. This requires the Bazel build system. It is highly recommended that you install
the nightly build of TensorFlow (tf-nightly
) before trying to build
TensorFlow Probability from source.
# sudo apt-get install bazel git python-pip # Ubuntu; others, see above links.
python -m pip install --upgrade --user tf-nightly
git clone https://github.com/tensorflow/probability.git
cd probability
bazel build --copt=-O3 --copt=-march=native :pip_pkg
PKGDIR=$(mktemp -d)
./bazel-bin/pip_pkg $PKGDIR
python -m pip install --upgrade --user $PKGDIR/*.whl
As part of TensorFlow, we're committed to fostering an open and welcoming environment.
See the TensorFlow Community page for more details. Check out our latest publicity here:
We're eager to collaborate with you! See CONTRIBUTING.md
for a guide on how to contribute. This project adheres to TensorFlow's
code of conduct. By participating, you are expected to
uphold this code.
If you use TensorFlow Probability in a paper, please cite:
(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.)