10 reasons why PyTorch is one of the most popular Deep Learning Frameworks

It doesn’t matter whether you’re a student, a researcher, a data scientist, a machine learning engineer, or just a machine learning enthusiast, you’re surely going to find PyTorch very useful.

As you might be aware, PyTorch is an open source Machine Learning library used primarily for applications such as computer vision and natural language processing.

PyTorch is a strong player in the field of Deep Learning and Artificial Intelligence, and it can be considered primarily as a research-first library.

Lets’s take a look at the top 10 reasons why PyTorch is one of the most popular Deep Learning Frameworks out there

1. PyTorch is Pythonic

Python is one of the most popular language used by Data Scientists. When I use the term “Pythonic”, I mean that PyTorch is more attached to or leaning towards Python as its primary programming language. Perhaps not coincidentally, Python is also one of the most popular languages used for building Machine Learning models and for ML research.

PyTorch is built to be seamlessly integrated with Python and its popular libraries like NumPy. Check out the code snippet below to see how easy it is to manipulate a NumPy array using PyTorch:

2. Easy to learn

PyTorch is comparatively easier to learn than other Deep Learning Frameworks. This is because its syntax and application are similar to many conventional programming languages like Python.

PyTorch’s documentation is also very organized and helpful for beginners. And a focused community of developers are also helping to continuously improve PyTorch.

Defining Machine Learning Model in PyTorch

As you can see above, defining a Machine Learning model in PyTorch is as easy as defining a class in Python, with just a few methods.

Photo by Tim Mossholder on Unsplash

3. Higher developer productivity

PyTorch is very simple to use, which also means that the learning curve for developers is relatively short.

PyTorch has a simple Python interface and provides a simple yet powerful API. PyTorch can also be easily implemented on both Windows and Linux.

PyTorch is easier to learn as it is not a completely different syntax for someone coming from programming background. The code below demonstrates that general programming practices like implementing variables, random numbers, square, and multiplication syntax are all very intuitive.

PyTorch Simple Syntax

4. Easy debugging

As PyTorch is deeply integrated with Python, many Python debugging tools can also be used in PyTorch code. Specifically, Python’s pdb and ipdb tools can be used for this kind of debugging in PyTorch.

Python’s IDE PyCharm also has a debugger can also be used to debug PyTorch code. All this is possible as a computational graph in PyTorch that’s defined at runtime.

If you’re getting error in your code, you can start debugging by placing breakpoints using pdb.set_trace() at any appropriate line in your code. Then you can execute further computations and check the PyTorch Tensors or variables and nail down the root cause of the error.

5. Data Parallelism

PyTorch has a very useful feature known as data parallelism. Using this feature, PyTorch can distribute computational work among multiple CPU or GPU cores.

This feature of PyTorch allows us to use torch.nn.DataParallel to wrap any module and helps us do parallel processing over batch dimension.

PyTorch Data Parallel

6. Dynamic Computational Graph Support

PyTorch supports dynamic computational graphs, which means the network behavior can be changed programmatically at runtime. This facilitates more efficient model optimization and gives PyTorch a major advantage over other Machine Learning Frameworks, which treat neural networks as static objects.

With this dynamic approach, we can fully see each and every computation and know exactly what is going on.

When the flow of data and the corresponding operations are defined at runtime, the construction of the computational graph happens dynamically. This is done with the help of autograd class implicitly, as shown below:

PyTorch requires_grad example

7. Hybrid Front-End

PyTorch also provides a new hybrid front-end. This means we have two modes of operation, namely eager mode and graph mode.

We generally use eager mode for research and development, as this mode provides flexibility and ease of use. And we generally use graph mode for production, as this provides better speed, optimization, and functionality in a C++ runtime environment.

PyTorch Train and eval mode

8. Useful Libraries

A large community of developers have built many tools and libraries for extending PyTorch. The community is also supporting development in computer vision, reinforcement learning, and much more.

This will surely bolster PyTorch’s reach as a fully-featured deep learning library for both research and production purposes. Here are a few examples of popular libraries:

  • GPyTorch is a highly efficient and modular implementation with GPU acceleration. It’s implemented in PyTorch and combines Gaussian processes with deep neural networks.
  • BoTorch is a PyTorch-related library for Bayesian optimization.
  • AllenNLP is an open-source NLP research library, built on PyTorch.

Photo by Eliabe Costa on Unsplash

9. Open Neural Network Exchange support

ONNX stands for Open Neural Network Exchange. With ONNX, AI developers can easily move models between different tools and choose the combination that work best for them and their given use case.

PyTorch has native ONNX support and can export models in the standard Open Neural Network Exchange format.

This will enable PyTorch-based models to direct access the ONNX-compatible platforms and run-times.

PyTorch Open Neural Network Exchange ONNX

10. Cloud support

PyTorch is also well-received by major cloud platforms, allowing developers and engineers to do large-scale training jobs on GPUs with PyTorch.

PyTorch’s cloud support also provides the ability to run models in a production environment. Not only that, we can also scale our PyTorch model using the cloud. For example, you can use the below code to work with PyTorch using AWS Sagemaker.

Concluding Thoughts

We now have ten strong points to support our decision to select PyTorch as our preferred deep learning framework.

I would also like to mention that PyTorch provides excellent community support with a highly active developer community as well.

I hope these points will motivate you to try building a machine learning model using PyTorch.

Happy Machine Learning!!


#machine-learning #python #numpy #data-science #deep-learning

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10 reasons why PyTorch is one of the most popular Deep Learning Frameworks
Margaret D

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Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

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Mikel  Okuneva

Mikel Okuneva

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Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


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PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Samanta  Moore

Samanta Moore

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Top 10 Popular Java Frameworks Every Developer Should Know in 2021

Java frameworks are essentially blocks of pre-written code, to which a programmer may add his code to solve specific problems. Several Java frameworks exist, all of which have their pros and cons. All of them can be used to solve problems in a variety of fields and domains. Java frameworks reduce the amount of coding from scratch that programmers have to do to come up with a solution.

Table of Contents

#full stack development #frameworks #java #java frameworks #top 10 popular java frameworks every developer should know in 2021 #top 10 popular java frameworks

10 reasons why PyTorch is one of the most popular Deep Learning Frameworks

It doesn’t matter whether you’re a student, a researcher, a data scientist, a machine learning engineer, or just a machine learning enthusiast, you’re surely going to find PyTorch very useful.

As you might be aware, PyTorch is an open source Machine Learning library used primarily for applications such as computer vision and natural language processing.

PyTorch is a strong player in the field of Deep Learning and Artificial Intelligence, and it can be considered primarily as a research-first library.

Lets’s take a look at the top 10 reasons why PyTorch is one of the most popular Deep Learning Frameworks out there

1. PyTorch is Pythonic

Python is one of the most popular language used by Data Scientists. When I use the term “Pythonic”, I mean that PyTorch is more attached to or leaning towards Python as its primary programming language. Perhaps not coincidentally, Python is also one of the most popular languages used for building Machine Learning models and for ML research.

PyTorch is built to be seamlessly integrated with Python and its popular libraries like NumPy. Check out the code snippet below to see how easy it is to manipulate a NumPy array using PyTorch:

2. Easy to learn

PyTorch is comparatively easier to learn than other Deep Learning Frameworks. This is because its syntax and application are similar to many conventional programming languages like Python.

PyTorch’s documentation is also very organized and helpful for beginners. And a focused community of developers are also helping to continuously improve PyTorch.

Defining Machine Learning Model in PyTorch

As you can see above, defining a Machine Learning model in PyTorch is as easy as defining a class in Python, with just a few methods.

Photo by Tim Mossholder on Unsplash

3. Higher developer productivity

PyTorch is very simple to use, which also means that the learning curve for developers is relatively short.

PyTorch has a simple Python interface and provides a simple yet powerful API. PyTorch can also be easily implemented on both Windows and Linux.

PyTorch is easier to learn as it is not a completely different syntax for someone coming from programming background. The code below demonstrates that general programming practices like implementing variables, random numbers, square, and multiplication syntax are all very intuitive.

PyTorch Simple Syntax

4. Easy debugging

As PyTorch is deeply integrated with Python, many Python debugging tools can also be used in PyTorch code. Specifically, Python’s pdb and ipdb tools can be used for this kind of debugging in PyTorch.

Python’s IDE PyCharm also has a debugger can also be used to debug PyTorch code. All this is possible as a computational graph in PyTorch that’s defined at runtime.

If you’re getting error in your code, you can start debugging by placing breakpoints using pdb.set_trace() at any appropriate line in your code. Then you can execute further computations and check the PyTorch Tensors or variables and nail down the root cause of the error.

5. Data Parallelism

PyTorch has a very useful feature known as data parallelism. Using this feature, PyTorch can distribute computational work among multiple CPU or GPU cores.

This feature of PyTorch allows us to use torch.nn.DataParallel to wrap any module and helps us do parallel processing over batch dimension.

PyTorch Data Parallel

6. Dynamic Computational Graph Support

PyTorch supports dynamic computational graphs, which means the network behavior can be changed programmatically at runtime. This facilitates more efficient model optimization and gives PyTorch a major advantage over other Machine Learning Frameworks, which treat neural networks as static objects.

With this dynamic approach, we can fully see each and every computation and know exactly what is going on.

When the flow of data and the corresponding operations are defined at runtime, the construction of the computational graph happens dynamically. This is done with the help of autograd class implicitly, as shown below:

PyTorch requires_grad example

7. Hybrid Front-End

PyTorch also provides a new hybrid front-end. This means we have two modes of operation, namely eager mode and graph mode.

We generally use eager mode for research and development, as this mode provides flexibility and ease of use. And we generally use graph mode for production, as this provides better speed, optimization, and functionality in a C++ runtime environment.

PyTorch Train and eval mode

8. Useful Libraries

A large community of developers have built many tools and libraries for extending PyTorch. The community is also supporting development in computer vision, reinforcement learning, and much more.

This will surely bolster PyTorch’s reach as a fully-featured deep learning library for both research and production purposes. Here are a few examples of popular libraries:

  • GPyTorch is a highly efficient and modular implementation with GPU acceleration. It’s implemented in PyTorch and combines Gaussian processes with deep neural networks.
  • BoTorch is a PyTorch-related library for Bayesian optimization.
  • AllenNLP is an open-source NLP research library, built on PyTorch.

Photo by Eliabe Costa on Unsplash

9. Open Neural Network Exchange support

ONNX stands for Open Neural Network Exchange. With ONNX, AI developers can easily move models between different tools and choose the combination that work best for them and their given use case.

PyTorch has native ONNX support and can export models in the standard Open Neural Network Exchange format.

This will enable PyTorch-based models to direct access the ONNX-compatible platforms and run-times.

PyTorch Open Neural Network Exchange ONNX

10. Cloud support

PyTorch is also well-received by major cloud platforms, allowing developers and engineers to do large-scale training jobs on GPUs with PyTorch.

PyTorch’s cloud support also provides the ability to run models in a production environment. Not only that, we can also scale our PyTorch model using the cloud. For example, you can use the below code to work with PyTorch using AWS Sagemaker.

Concluding Thoughts

We now have ten strong points to support our decision to select PyTorch as our preferred deep learning framework.

I would also like to mention that PyTorch provides excellent community support with a highly active developer community as well.

I hope these points will motivate you to try building a machine learning model using PyTorch.

Happy Machine Learning!!


#machine-learning #python #numpy #data-science #deep-learning