Forwarded from Computer Science and Programming
https://dafriedman97.github.io/mlbook/content/table_of_contents.html
And The list of Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: Sept 9, 2020):
https://www.marktechpost.com/free-resources/?fbclid=IwAR0hc2qkxPMXhQGzsg07ffgFecRr01tSCRqlhb_XMR6PjPt1KNdy68cLy9w
And The list of Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: Sept 9, 2020):
https://www.marktechpost.com/free-resources/?fbclid=IwAR0hc2qkxPMXhQGzsg07ffgFecRr01tSCRqlhb_XMR6PjPt1KNdy68cLy9w
Forwarded from Gadget and device News 🗞️
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St Petersburg, Russia, 75 year after Victory in WWII celebration. Drone show
Forwarded from Data Science by ODS.ai 🦜
pytorch lightning bolts
from linear, logistic regression on tpu-s to pre-trained gan-s
plb – is a collection of pytorch lightning implementations of popular models that are well tested and optimized for speed on multiple gpu-s and tpu-s
it is a new community built dl research and production toolbox, featuring a collection of well established and sota models and components, pre-trained weights, callbacks, loss functions, data sets, and data modules
everything is implemented in lightning and tested benchmarked, documented, and works on cpu-s, tpu-s, gpu-s, and 16-bit precision
more u can read at the blog post
github: https://github.com/PyTorchLightning/pytorch-lightning-bolts
#pytorchlightning #bolts #multiple
from linear, logistic regression on tpu-s to pre-trained gan-s
plb – is a collection of pytorch lightning implementations of popular models that are well tested and optimized for speed on multiple gpu-s and tpu-s
it is a new community built dl research and production toolbox, featuring a collection of well established and sota models and components, pre-trained weights, callbacks, loss functions, data sets, and data modules
everything is implemented in lightning and tested benchmarked, documented, and works on cpu-s, tpu-s, gpu-s, and 16-bit precision
more u can read at the blog post
github: https://github.com/PyTorchLightning/pytorch-lightning-bolts
#pytorchlightning #bolts #multiple
Forwarded from Binary Tree
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TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
#ts, #python, #tensorflow, #machine, #learning, #visualization
#ts, #python, #tensorflow, #machine, #learning, #visualization
Forwarded from Machinelearning
Understanding the Role of Individual Units in a Deep Neural Network
Examine two types of networks that contain interpretable units: networks trained to classify images of scenes, and networks trained to synthesize images of scenes.
https://dissect.csail.mit.edu/
Github: https://github.com/davidbau/dissect
Website: https://www.pnas.org/content/early/2020/08/31/1907375117
Paper: https://arxiv.org/pdf/2009.05041.pdf
Examine two types of networks that contain interpretable units: networks trained to classify images of scenes, and networks trained to synthesize images of scenes.
https://dissect.csail.mit.edu/
Github: https://github.com/davidbau/dissect
Website: https://www.pnas.org/content/early/2020/08/31/1907375117
Paper: https://arxiv.org/pdf/2009.05041.pdf
Forwarded from Spark in me (Alexander)
Silero Speech-To-Text Models V1 Released
We are proud to announce that we have released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:
- English
- German
- Spanish
Why this is a big deal:
- STT Research is typically focused on huge compute budgets
- Pre-trained models and recipes did not generalize well, were difficult to use even as-is, relied on obsolete tech
- Until now STT community lacked easy to use high quality production grade STT models
How we solve it:
- We publish a set of pre-trained high-quality models for popular languages
- Our models are embarrassingly easy to use
- Our models are fast and can be run on commodity hardware
Even if you do not work with STT, please give us a star / share!
Links
- https://github.com/snakers4/silero-models
We are proud to announce that we have released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:
- English
- German
- Spanish
Why this is a big deal:
- STT Research is typically focused on huge compute budgets
- Pre-trained models and recipes did not generalize well, were difficult to use even as-is, relied on obsolete tech
- Until now STT community lacked easy to use high quality production grade STT models
How we solve it:
- We publish a set of pre-trained high-quality models for popular languages
- Our models are embarrassingly easy to use
- Our models are fast and can be run on commodity hardware
Even if you do not work with STT, please give us a star / share!
Links
- https://github.com/snakers4/silero-models
GitHub
GitHub - snakers4/silero-models: Silero Models: pre-trained text-to-speech models made embarrassingly simple
Silero Models: pre-trained text-to-speech models made embarrassingly simple - snakers4/silero-models
Forwarded from Binary Tree
Forwarded from Machinelearning
Implementing a Deep Learning Library from Scratch in Python
https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html
https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html
KDnuggets
Implementing a Deep Learning Library from Scratch in Python - KDnuggets
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
Forwarded from Binary Tree
Forwarded from Machinelearning
🔋 The Most Complete Guide to PyTorch for Data Scientists
https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html
Code: https://github.com/MLWhiz/data_science_blogs/tree/master/pytorch_guide
@ai_machinelearning_big_data
https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html
Code: https://github.com/MLWhiz/data_science_blogs/tree/master/pytorch_guide
@ai_machinelearning_big_data
KDnuggets
The Most Complete Guide to PyTorch for Data Scientists - KDnuggets
All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.
Forwarded from Binary Tree
Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Ignite is a library that provides three high-level features:
- Extremely simple engine and event system
- Out-of-the-box metrics to easily evaluate models
- Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics
#python, #pytorch, #ml, #neural, #network
Ignite is a library that provides three high-level features:
- Extremely simple engine and event system
- Out-of-the-box metrics to easily evaluate models
- Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics
#python, #pytorch, #ml, #neural, #network
Forwarded from Linuxgram 🐧
KeenWrite: An Open Source Text Editor for Data Scientists
https://itsfoss.com/keenwrite/
https://itsfoss.com/keenwrite/
It's FOSS
KeenWrite: An Open Source Text Editor for Data Scientists and Mathematicians
An interesting text editor that supports R markdown. With codes in R, you can add string interpolation, graphs and other mathematical expressions to your text document.
Forwarded from Machinelearning
Introduction to Pytorch Code Examples
An overview of training, models, loss functions and optimizers
Free course: https://cs230.stanford.edu/blog/pytorch/
Lectures: https://cs230.stanford.edu/lecture/
Github: https://github.com/thanhhff/CS230-Deep-Learning
@ai_machinelearning_big_data
An overview of training, models, loss functions and optimizers
Free course: https://cs230.stanford.edu/blog/pytorch/
Lectures: https://cs230.stanford.edu/lecture/
Github: https://github.com/thanhhff/CS230-Deep-Learning
@ai_machinelearning_big_data
Forwarded from Machinelearning
Graph-based Neural Structured Learning in TFX
New learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs.
https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning
Article: https://blog.tensorflow.org/2020/10/neural-structured-learning-in-tfx.html
Neural Structured Learning: https://www.tensorflow.org/neural_structured_learning
Github: https://github.com/tensorflow/neural-structured-learning#videos-and-colab-tutorials
@ai_machinelearning_big_data
New learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs.
https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning
Article: https://blog.tensorflow.org/2020/10/neural-structured-learning-in-tfx.html
Neural Structured Learning: https://www.tensorflow.org/neural_structured_learning
Github: https://github.com/tensorflow/neural-structured-learning#videos-and-colab-tutorials
@ai_machinelearning_big_data
Forwarded from PDP-11🚀
NIVIDIA DPU
by Forbes
⚡️Domain specific processors (accelerators) are playing greater roles in off-loading CPUs and improving performance of computing systems
🤖The NVDIA BlueField-2 DPU( Data Processor Units), a new domain specific computing technology, is enabled by the company’s Data-Center-Infrastructure-on-a-Chip Software (DOCA SDK) Smart NIC. . Off-loading processing to a DPU can result in overall cost savings and improved performance for data centers.
👬 NVIDIA’s current DPU lineup includes two PCIe cards BlueField-2 and BlueField-2X DPUs. BlueField-2 based on ConnectX®-6 Dx SmartNIC combined with powerful Arm cores. BlueField-2X includes all the key features of a BlueField-2 DPU enhanced with an NVIDIA Ampere GPU’s AI capabilities that can be applied to data center security, networking and storage tasks
Read more about DPUs:
- Product page
- Mellanox product brief
- Servethehome
- Nextplatform
- Nextplatform
by Forbes
⚡️Domain specific processors (accelerators) are playing greater roles in off-loading CPUs and improving performance of computing systems
🤖The NVDIA BlueField-2 DPU( Data Processor Units), a new domain specific computing technology, is enabled by the company’s Data-Center-Infrastructure-on-a-Chip Software (DOCA SDK) Smart NIC. . Off-loading processing to a DPU can result in overall cost savings and improved performance for data centers.
👬 NVIDIA’s current DPU lineup includes two PCIe cards BlueField-2 and BlueField-2X DPUs. BlueField-2 based on ConnectX®-6 Dx SmartNIC combined with powerful Arm cores. BlueField-2X includes all the key features of a BlueField-2 DPU enhanced with an NVIDIA Ampere GPU’s AI capabilities that can be applied to data center security, networking and storage tasks
Read more about DPUs:
- Product page
- Mellanox product brief
- Servethehome
- Nextplatform
- Nextplatform