Learn All About Tensorflow Vs. Pytorch: Which one Is for Your Project

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.

#tensorflow #pytorch 

Pytorch Vs TensorFlow: Comparing Deep Learning Frameworks

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.
 

#pytorch #tensorflow 

Fully Understand Deep Reinforcement Learning Algorithms with Pytorch

This repository contains PyTorch implementations of deep reinforcement learning algorithms and environments.
#pytorch #deep-learning 

Hands-On Guide To PyKale: A Python Tool for Multimodal and Transfer Learning

#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/

Multi Agent Deep Deterministic Policy Gradients (MADDPG) in PyTorch

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.

https://youtu.be/tZTQ6S9PfkE

#reinforcement-learning #pytorch #python #deep-learning #machine-learning #artificial-intelligence

Asynchronous Advantage Actor Critic (A3C) Tutorial

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.

https://youtu.be/OcIx_TBu90Q

#pytorch #reinforcement-learning #deep-learning #artificial-intelligence #machine-learning #python

8 Free Resources To Learn PyTorch In 2021

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

JAX Vs TensorFlow Vs PyTorch: A Comparative Analysis

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

An Ideal Way to Study Deep Learning with PyTorch on MS Learn

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

Introduction to Deep Learning (DL) with PyTorch

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.

Powered by Restream https://restream.io/

#pytorch #deep-learning #python #data-science #developer

Hands-On Workshop: Accelerate PyTorch Applications Using Intel oneAPI Toolkit

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.

Read more: https://analyticsindiamag.com/hands-on-workshop-accelerate-pytorch-applications-using-intel-oneapi-toolkit/

#workshop #intel #pytorch #oneapi

PyTorch Fundamentals in Microsoft Learn

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

1D Tensors | numpy arrays pandas series python list | Deep Learning with PyTorch

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

Densely Connected Time Delay Neural Network for Speaker Verification

Densely Connected Time Delay Neural Network

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).

What’s New ⚠️

• [2021-02-14] We add an impl option in TimeDelay, now you can choose:

  • ‘conv’: implement TDNN by F.conv1d.
  • ‘linear’: implement TDNN by F.unfold and F.linear.

  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:

  • TDNN in this repo was also used to create F-TDNN models that are not perfectly supported by nn.Conv1d (asymmetric paddings).
  • nn.Conv1d(dilation>1, bias=True) is slow in training.

  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.

Pretrained Models

We provide the pretrained models which can be used in many tasks such as:

• Speaker Verification
• Speaker-Dependent Speech Separation
• Multi-Speaker Text-to-Speech
• Voice Conversion

Usage

Data preparation

You can either use Kaldi toolkit:

• Download VoxCeleb1 test set and unzip it.
• Place prepare_voxceleb1_test.sh under $kaldi_root/egs/voxceleb/v2 and change the $datadir and $voxceleb1_root in it.
• Run chmod +x prepare_voxceleb1_test.sh && ./prepare_voxceleb1_test.sh to generate 30-dim MFCCs.
• Place the trials under $datadir/test_no_sil.

Or checkout the kaldifeat branch if you do not want to install Kaldi.

Test

• Download the pretrained D-TDNN model and run:

python evaluate.py --root $datadir/test_no_sil --model D-TDNN --checkpoint dtdnn.pth --device cuda

Evaluation

VoxCeleb1-O

Citation

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}
}

Revision of the Paper ⚠️

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.

Download Details:

Author: yuyq96
Download Link: Download The Source Code
Official Website: https://github.com/yuyq96/D-TDNN

#pytorch #python #data-science

How To Install and Use PyTorch in 100 seconds

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.

Did I help you out?
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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
🎙 - Twitter: https://twitter.com/antonvputra
📨 - Email: me@antonputra.com

Original Article: https://www.digitalocean.com/community/tutorials/how-to-install-and-use-pytorch

#PyTorch #Python #DevOps

#devops #python #pytorch