ββTensorFlow Quantum
A Software Framework for Quantum Machine Learning
Introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
TFQ provides the tools necessary for bringing the quantum computing and ML research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50-100 qubits.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced β quantum data and hybrid quantum-classical models.
Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated/simulated on quantum processors/sensors/networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
Quantum models cannot use quantum processors alone β NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.
To build and train such a model, the researcher can do the following:
β prepare a quantum dataset
β evaluate a quantum NN model
- sample or Average
β evaluate a classical NN model
β evaluate Ρost function
β evaluate gradients & update parameters
blog post: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
paper: https://arxiv.org/abs/2003.02989
#tfq #tensorflow #quantum #physics #ml
A Software Framework for Quantum Machine Learning
Introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
TFQ provides the tools necessary for bringing the quantum computing and ML research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50-100 qubits.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced β quantum data and hybrid quantum-classical models.
Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated/simulated on quantum processors/sensors/networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
Quantum models cannot use quantum processors alone β NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.
To build and train such a model, the researcher can do the following:
β prepare a quantum dataset
β evaluate a quantum NN model
- sample or Average
β evaluate a classical NN model
β evaluate Ρost function
β evaluate gradients & update parameters
blog post: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
paper: https://arxiv.org/abs/2003.02989
#tfq #tensorflow #quantum #physics #ml
ββtrain your tf models on google cloud by tensorflow cloud
tf cloud is a python package that provides api for a transition from debugging and training keras & tf code in the local environment to distributed training in google cloud. it simplifies the process of training models on the cloud into a single, simple function call, requiring minimal setup and almost zero changes to model.
tf cloud handles cloud-specific tasks such as creating vm instances and distribution strategies for models automatically.
blog post: https://blog.tensorflow.org/2020/08/train-your-tensorflow-model-on-google.html?linkId=95907203
github: https://github.com/tensorflow/cloud
#tensorflow #cloud
tf cloud is a python package that provides api for a transition from debugging and training keras & tf code in the local environment to distributed training in google cloud. it simplifies the process of training models on the cloud into a single, simple function call, requiring minimal setup and almost zero changes to model.
tf cloud handles cloud-specific tasks such as creating vm instances and distribution strategies for models automatically.
blog post: https://blog.tensorflow.org/2020/08/train-your-tensorflow-model-on-google.html?linkId=95907203
github: https://github.com/tensorflow/cloud
#tensorflow #cloud
ββLanguage-agnostic BERT Sentence Embedding
Authors adopt multilingual BERT to produce language-agnostic sentence embeddings for 109 languages.
The model combines a masked language model (MLM) and a translation language model (TLM) pretraining with a translation ranking task using bi-directional dual encoders.
The resulting multilingual sentence embeddings improve average bi-text retrieval accuracy over 112 languages to 83.7% on Tatoeba (previous state-of-the-art was 65.5%)
blogpost: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
paper: https://arxiv.org/abs/2007.01852
bodel on tf hub: https://tfhub.dev/google/LaBSE/1
#deeplearning #transformers #nlp #tensorflow #sentenceembeddings
Authors adopt multilingual BERT to produce language-agnostic sentence embeddings for 109 languages.
The model combines a masked language model (MLM) and a translation language model (TLM) pretraining with a translation ranking task using bi-directional dual encoders.
The resulting multilingual sentence embeddings improve average bi-text retrieval accuracy over 112 languages to 83.7% on Tatoeba (previous state-of-the-art was 65.5%)
blogpost: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
paper: https://arxiv.org/abs/2007.01852
bodel on tf hub: https://tfhub.dev/google/LaBSE/1
#deeplearning #transformers #nlp #tensorflow #sentenceembeddings
Lo-Fi Player
The team from the magenta project, that does research about deep learning and music powered by TensorFlow in Google, obviously, release a new fun project lofi-player powered by their open-source library magenta.js.
So it's basically a lo-fi music generator which popular genre on youtube streams and other kinds of stuff. You can customize the vibe on your manner and wish from sad to moody, slow to fast, etc.
It is based on their earlier work MusicVae to sample latent space of music and MelodyRNN to generate music sequences from different instruments. The project is not about new research, but to show what can do with an already done library in a creative way.
They also create a stream on youtube to listen lo-fi generated by that application and users in chat can together tune lo-fi player in stream :)
#magenta #lo-fi #music #google #tensorflow #fun
The team from the magenta project, that does research about deep learning and music powered by TensorFlow in Google, obviously, release a new fun project lofi-player powered by their open-source library magenta.js.
So it's basically a lo-fi music generator which popular genre on youtube streams and other kinds of stuff. You can customize the vibe on your manner and wish from sad to moody, slow to fast, etc.
It is based on their earlier work MusicVae to sample latent space of music and MelodyRNN to generate music sequences from different instruments. The project is not about new research, but to show what can do with an already done library in a creative way.
They also create a stream on youtube to listen lo-fi generated by that application and users in chat can together tune lo-fi player in stream :)
#magenta #lo-fi #music #google #tensorflow #fun
Lo-Fi Player
Interactive lofi beat player.
ββTutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow
Nice tutorial with enough theory to understand what you are doing and code to get it done.
Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/
#Keras #TensorFlow #tutorial #wheretostart #GAN
Nice tutorial with enough theory to understand what you are doing and code to get it done.
Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/
#Keras #TensorFlow #tutorial #wheretostart #GAN
