Search Results for "multi-layer perceptron python"
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Showing 4 open source projects for "multi-layer perceptron python"
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BoldTrail Real Estate CRMBoldTrail, the #1 rated real estate platform, is built to power your entire brokerage with next-generation technology your agents will use and love. Showcase your unique brand with customizable websites for your company, offices, and every agent. Maximize lead capture with a modern, portal-like consumer search experience and intelligent behavior tracking. Hyper-local area pages, home valuation pages and options for rich lifestyle data keep customers searching with your brokerage as the local experts. The most robust lead gen tools on the market help your brokerage, teams & agents effectively drive new business - no matter their budget. Empower your agents to generate free leads instantly with our simple to use landing pages & IDX squeeze pages. Drive more leads with higher quality and lower cost through in-house tools built within the platform. Diversify lead sources with our automated social media posting, integrated Google and Facebook advertising, custom text codes and more.
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Financial reporting cloud-based software.The software is cloud based and automates complexities around consolidating and reporting for groups with multiple year ends, currencies and ERP systems with a slice and dice approach to reporting. While retaining the structure, control and validation needed in a financial reporting tool, we’ve managed to keep things flexible.
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI... -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ... -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the... -
Cloud data warehouse to power your data-driven innovationBigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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