Prophet VS tensorflow

My rescue came from discovering some of the alternatives to ARIMA/SARIMA, which was the extent of what we had covered for time series data. A series of searches eventually led me to some automated time series analysis packages, one of which was Prophet, an open source time series package released by Facebook's core data science team. This was a life saver, being a much more efficient and more effective forecasting tool than sloooowly iterating through ARIMA/SARIMA models that seemed to want to fight with me. If you're going to do a time series analysis for your capstone, I strongly suggest taking a look at using Prophet.

Prophet

Prophet

Prophet: Automação preditiva.

Prophet - It is a time-series forecasting library built by Facebook. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Link - https://github.com/facebook/prophet

We run an anomaly detection app powered by Facebook's Prophet forecasting library. It tells us if metrics dip or rise in unexpected ways ("Did signups drop? Is something broken with that flow?"). We built the service because customers kept reaching out to tell us some feature broke before we noticed. Normally these issues show up in product data, so the app looks for these anomalies and tells us when they happen.

This is indeed the bread-and-butter, but there is use of all sorts of standard linear algebra algorithms. You can check various xla-related (accelerated linear algebra) folders in tensorflow or torch folders in pytorch to see the list of what is used [1],[2]

[1] https://github.com/tensorflow/tensorflow/tree/8d9b35f442045b...

[2] https://github.com/pytorch/pytorch/blob/6e3e3dd477e0fb9768ee...

2. TensorFlow Developed by the Google Brain team, TensorFlow is a powerful open-source machine learning framework that’s perfect for deep learning and neural network projects. With TensorFlow, you can build and train complex models using an intuitive and flexible API, making it an essential tool for any data scientist looking to delve into deep learning.

I found the official Tensorflow V1 code from a Github branch here (https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/models/rnn/ptb/ptb_word_lm.py). All code necessary to run that file is in the /ptb folder (except data).

In their report they show the 10 projects with the biggest number of contributors. The first one is microsoft/vscode with 19.8K contributors in 2022 and the 10th place is tensorflow/tensorflow with 4.4K contributors. That's really nice, but my guess is that most repositories have very few contributors.

TensorFlow - This one needs no introduction. It’s widely-used and it has several tools and community resources for training and deploying ML/DL models. This one is JS based and I’m not too familiar with JS except that I know a lot of people use it for web development. What I like about it is that it’s used for speech and image recognition. And one of my favorites are text summarization.

If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....

In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.

I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.

Install necessary dependencies: sudo apt-get update sudo apt-get upgrade sudo apt-get install build-essential git libhdf5-dev python-dev python-pip python-numpy python-wheel python-mock python-mockito python-pytest python-six python-h5py sudo pip install --upgrade pip sudo pip install setuptools Install Bazel: sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python wget https://github.com/bazelbuild/bazel/releases/download/0.26.1/bazel-0.26.1-installer-linux-x86_64.sh chmod +x bazel-0.26.1-installer-linux-x86_64.sh ./bazel-0.26.1-installer-linux-x86_64.sh --user Clone TensorFlow 1.x: git clone https://github.com/tensorflow/tensorflow.git cd tensorflow git checkout r1.15 Configure TensorFlow: ./configure Build TensorFlow: bazel build --config=opt --local_resources=1024,1.0,1.0 --verbose_failures //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg sudo pip install /tmp/tensorflow_pkg/tensorflow-1.15.0-cp27-cp27mu-linux_armv7l.whl Set up swap memory: sudo fallocate -l 4G /swapfile sudo chmod 600 /swapfile sudo mkswap /swapfile sudo swapon /swapfile Test TensorFlow: python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Heck, I don't know how involved you feel like getting, but providing Python 3.11 support for tensorflow would be super awesome.

Uite ce scrie la limbaj aici: https://github.com/tensorflow/tensorflow