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Deep learning is a common technique of solving real-world problems using human-like computers.
Deep learning tasks involve the creation of special brain-like architectures known as artificial neural networks.
To help develop such architectures, various Python frameworks for performing deep learning tasks have been developed, making it easier to build and train diversified artificial neural networks.
«Which one is better?»
This forms the subject of discussion in this tutorial.
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PyTorch and Tensorflow are among the most popular libraries for deep learning, which is a subfield of machine learning. Similarly to the way human brains process information, deep learning structures algorithms into layers creating deep artificial neural networks, which it can learn and make decisions on its own.
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This repository contains PyTorch implementations of deep reinforcement learning algorithms and environments.
#pytorch #deep-learning
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#tutorial
This article introduces pykale, a python library based on PyTorch that leverages knowledge from multiple sources for interpretable and accurate predictions in machine learning. This library consists of three objectives of green machine learning:
#pytorch
Read more: https://analyticsindiamag.com/hands-on-guide-to-pykale-a-python-tool-for-multimodal-and-transfer-learning/
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Multi agent deep deterministic policy gradients is one of the first successful algorithms for multi agent artificial intelligence. Cooperation and competition among AI agents is going to be critical as applications of deep learning expand in our daily lives. In this tutorial, we are going to read through the paper together and then code up the entire multi agent actor critic algorithm from scratch in the Pytorch framework.
The main innovation of this algorithm is the use of centralized execution and decentralized training. In brief, we’re going to give each agent’s critic network access to the observations and actions of all the agents in the simulation. The actor networks will only have access to their own perspective, hence the centralized execution.
We are going to use Open AI’s multi agent particle environment for training and testing our agents. I’ll show you how to get it from github and install the requirements in a virtual environment. We’ll cover some of the ways in which the new environments differ from the classic Open AI gym environments, and then we’re off to coding our agents.
#reinforcement-learning #pytorch #python #deep-learning #machine-learning #artificial-intelligence
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Asynchronous advantage actor critic methods are a particular variant of asynchronous deep reinforcement learning that takes a totally different approach to breaking correlations in the data we feed to our deep neural network.
Instead of using a replay buffer, we are going to use many independent agents in their own CPU thread, acting on independent environments. Each of these will collect experiences and help to update the global optimizer and a global actor critic agent. We’ll do “transfer learning” to update each of our local actor critics so that each can take advantage of the experience of the others.
#pytorch #reinforcement-learning #deep-learning #artificial-intelligence #machine-learning #python
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Developed by Facebook AI Research (FAIR), PyTorch is one of the most widely used open-source machine learning libraries for deep learning applications. It was first introduced in 2016. Since then, PyTorch has been gaining popularity among researchers and developers, at the expense of TensorFlow.
https://analyticsindiamag.com/8-free-resources-tools-to-learn-pytorch-in-2021/
#pytorch #deep-learning
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Deep learning owes a lot of its success to automatic differentiation. Popular libraries such as TensorFlow and PyTorch keep track of gradients over neural network parameters during training with both comprising high-level APIs for implementing the commonly used neural network functionality for deep learning. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research. Along with a Deep Learning framework, JAX has created a super polished linear algebra library with automatic differentiation and XLA support.
Read more: https://analyticsindiamag.com/jax-vs-tensorflow-vs-pytorch-a-comparative-analysis/
#tensorflow #pytorch
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Microsoft Learn recently enabled an important new way for you to get familiar with machine learning. You can now follow along and work with Microsoft Learn exercises using Jupyter Notebooks!
In this episode, we’ll discuss the collaboration with PyTorch that delivered this new functionality, plus review learning paths on Microsoft Learn that have Jupyter Notebooks enabled, what that means, and how you can use them to enhance your Microsoft Learn experience.
Speaker:
Dmitry Soshnikov
@shwars
Dmitry has been working for Microsoft for 15 years, having a career in evangelism, software development, data science and now in cloud advocacy. He is also an associate professor, and teaches AI and Functional Programming in a couple of universities in Russia.
#deep-learning #pytorch #python
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PyTorch Lightning reduces the engineering boilerplate and resources required to implement state-of-the-art AI. Organizing PyTorch code with Lightning enables seamless training on multiple GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision, and more, without any code changes. In this talk, we will start with a general overview of Deep Learning and then transition into practical Lightning examples to demonstrate how to train Deep Learning models with less boilerplate.
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#pytorch #deep-learning #python #data-science #developer
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Developers often struggle with high computational complexity, algorithmic challenge and inference of large neural networks while accelerating deep learning applications. PyTorch facilitates building deep learning projects by allowing the creation and training of deep neural networks and the ability to perform accelerated mathematical operations on dedicated hardware. PyTorch can be used to train a large amount of data at a rapid pace and is also flexible enough for experimentation and prototyping.
#workshop #intel #pytorch #oneapi
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Do you want to get familiar with machine learning frameworks like PyTorch? Check out the newly released PyTorch Fundamentals learning path featuring Jupyter notebook support in Microsoft Learn - and come get your questions answered with one of the creators of the learning path- Dmitry Soshnikov, Professor Amir Pourabdollah, and our Microsoft Student Ambassadors.
Dive into more on Microsoft Learn: https://aka.ms/PyTorchATELearn
Check out more episodes on demand: https://aka.ms/ATEonLearnTV
#pytorch #microsoft #python #data-science
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1D Tensors | numpy arrays pandas series python list | Deep Learning with PyTorch
Complete playlist - Deep Learning with PyTorch: https://www.youtube.com/playlist?list=PL1w8k37X_6L8oJGLWdzeOSRVTI6mL8vw7
#deeplearning #pytorch #tensors
#deeplearning #pytorch #tensors
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PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper “Densely Connected Time Delay Neural Network for Speaker Verification” (INTERSPEECH 2020).
[2021-02-14] We add an impl
option in TimeDelay, now you can choose:
Check this commit for more information. Note the pre-trained models of ‘conv’ have not been uploaded yet.
[2021-02-04] TDNN (default implementation) in this repo is slower than nn.Conv1d, but we adopted it because:
However, we do not use F-TDNN here, and we always set bias=False in D-TDNN. So, we are considering uploading a new version of TDNN soon (2021-02-14 updated).
[2021-02-01] Our new paper is accepted by ICASSP 2021.
Y.-Q. Yu, S. Zheng, H. Suo, Y. Lei, and W.-J. Li, “CAM: Context-Aware Masking for Robust Speaker Verification”
CAM
outperforms statistics-and-selection (SS) in terms of speed and accuracy.
We provide the pretrained models which can be used in many tasks such as:
You can either use Kaldi toolkit:
prepare_voxceleb1_test.sh
under $kaldi_root/egs/voxceleb/v2
and change the $datadir
and $voxceleb1_root
in it.chmod +x prepare_voxceleb1_test.sh && ./prepare_voxceleb1_test.sh
to generate 30-dim MFCCs.trials
under $datadir/test_no_sil
.Or checkout the kaldifeat branch if you do not want to install Kaldi.
python evaluate.py --root $datadir/test_no_sil --model D-TDNN --checkpoint dtdnn.pth --device cuda
VoxCeleb1-O
Model | Emb. | Params (M) | Loss | Backend | EER (%) | DCF_0.01 | DCF_0.001 |
---|---|---|---|---|---|---|---|
TDNN | 512 | 4.2 | Softmax | PLDA | 2.34 | 0.28 | 0.38 |
E-TDNN | 512 | 6.1 | Softmax | PLDA | 2.08 | 0.26 | 0.41 |
F-TDNN | 512 | 12.4 | Softmax | PLDA | 1.89 | 0.21 | 0.29 |
D-TDNN | 512 | 2.8 | Softmax | Cosine | 1.81 | 0.20 | 0.28 |
D-TDNN-SS (0) | 512 | 3.0 | Softmax | Cosine | 1.55 | 0.20 | 0.30 |
D-TDNN-SS | 512 | 3.5 | Softmax | Cosine | 1.41 | 0.19 | 0.24 |
D-TDNN-SS | 128 | 3.1 | AAM-Softmax | Cosine | 1.22 | 0.13 | 0.20 |
If you find D-TDNN helps your research, please cite
@inproceedings{DBLP:conf/interspeech/YuL20,
author = {Ya-Qi Yu and
Wu-Jun Li},
title = {Densely Connected Time Delay Neural Network for Speaker Verification},
booktitle = {Annual Conference of the International Speech Communication Association (INTERSPEECH)},
pages = {921--925},
year = {2020}
}
References:
[16] X. Li, W. Wang, X. Hu, and J. Yang, “Selective Kernel Networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 510-519.
Author: yuyq96
Download Link: Download The Source Code
Official Website: https://github.com/yuyq96/D-TDNN
#pytorch #python #data-science
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PyTorch is a framework developed by Facebook for deep learning.
Applications include computer vision, natural language processing, cryptography, and more.
In this tutorial, we install PyTorch and use image classifiers that have already been trained.
In particular, we will use image classifiers to predict the class of an image.
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=========
⏱️TIMESTAMPS⏱️
0:00 Intro
0:17 Create python virtual environment
0:25 Activate python virtual environment
0:27 Install PyTorch
0:35 Create JSON file to translate model predicted numeric results to human-readable text
0:48 Create python script
1:13 Run model to predict image class
=========
Source Code
🖥️ - GitHub: https://github.com/antonputra/tutorials/tree/main/lessons/067
=========
SOCIAL
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📨 - Email: me@antonputra.com
Original Article: https://www.digitalocean.com/community/tutorials/how-to-install-and-use-pytorch
#PyTorch #Python #DevOps
#devops #python #pytorch