Search Results for "units"
Sort By:
Showing 11 open source projects for "units"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
SentencePiece
Unsupervised text tokenizer for Neural Network-based text generation
SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre/postprocessing. Purely data driven, sentencePiece trains tokenization and detokenization models from sentences. ... -
2
GPU Puzzles
Solve puzzles. Learn CUDA
...The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively more complex puzzles, learners gain a practical understanding of how parallel algorithms operate on graphics processing units. The project emphasizes experimentation and problem solving, encouraging learners to discover GPU programming techniques through trial and exploration. It can be run in cloud environments such as Google Colab, making it easy for beginners to start experimenting without configuring local GPU hardware. -
3
Pedalboard
A Python library for audio
pedalboard is a Python library for working with audio: reading, writing, rendering, adding effects, and more. It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, pedalboard... -
4
Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
5
T81 558
Applications of Deep Neural Networks
...Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
6
MACE
Deep learning inference framework optimized for mobile platforms
...Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minimum external dependencies to keep the library footprint small. -
7
Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It... -
8
SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU... -
9
GluonNLP
NLP made easy
...Fasttext models trained with the library of Facebook research are exported both in text and a binary format. Unlike the text format, the binary format preserves information about subword units and consequently supports the computation of word vectors for words unknown during training (and not included in the text format). Besides training new fastText embeddings with Gluon NLP it is also possible to load the binary format into a Block provided by the Gluon NLP toolkit. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
11
Black Hole Cortex
Sphere surface layers of visual cortex approach maximum info density
Near the surface (even horizon) of a black hole, there is maximum information density in units of squared plancks (and some translation to qubits). Similarly, our imagination is the set of all possible things we can draw onto our most dense layer of visual cortex in electricity patterns. Bigger layers have more neurons to handle those possibilities. A Black Hole Cortex is a kind of visual cortex that has density of neuron layers similar to density at various radius from a black hole. ...
- Previous
- You're on page 1
- Next
