Wissam Muneer

Wissam Muneer

1601433642

Deep Learning Frameworks: MxNet vs TensorFlow vs DL4j vs PyTorch

It’s a great time to be a deep learning engineer. In this article, we will go through some of the popular deep learning frameworks like Tensorflow and CNTK so you can choose which one is best for your project.

Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are neural networks.

Deep learning is the technique of building complex multi-layered neural networks. This helps us solve tough problems like image recognition, language translation, self-driving car technology, and more.

There are tons of real-world applications of deep learning from self-driving Tesla cars to AI assistants like Siri. To build these neural networks, we use different frameworks like Tensorflow, CNTK, and MxNet.

If you are new to deep learning, start here for a good overview.

Frameworks

Without the right framework, constructing quality neural networks can be hard. With the right framework, you only have to worry about getting your hands on the right data.

That doesn’t imply that knowledge of the deep learning frameworks alone is enough to make you a successful data scientist.

You need a strong foundation of the fundamental concepts to be a successful deep learning engineer. But the right framework will make your life easier.

Also, not all programming languages have their own machine learning / deep learning frameworks. This is because not all programming languages have the capacity to handle machine learning problems.

Languages like Python stand out among others due to their complex data processing capability.

Let’s go through some of the popular deep learning frameworks in use today. Each one comes with its own set of advantages and limitations. It is important to have at least a basic understanding of these frameworks so you can choose the right one for your organization or project.

TensorFlow

TensorFlow is the most famous deep learning library around. If you are a data scientist, you probably started with Tensorflow. It is one of the most efficient open-source libraries to work with.

Google built TensorFlow to use as an internal deep learning tool before open-sourcing it. TensorFlow powers a lot of useful applications including Uber, Dropbox, and Airbnb.

Advantages of Tensorflow

  • User Friendly. Easy to learn if you are familiar with Python.
  • Tensorboard for monitoring and visualization. It is a great tool if you want to see your deep learning models in action.
  • Community support. Experts engineers from Google and other companies improve TensorFlow almost on a daily basis.
  • You can use TensorFlow Lite to run TensorFlow models on mobile devices.
  • Tensorflow.js lets you to run real-time deep learning models in the browser using JavaScript.

Limitations of Tensorflow

  • TensorFlow is a bit slow compared to frameworks like MxNet and CNTK.
  • Debugging can be challenging.
  • No support for OpenCL.

#deep-learning #machine-learning #tensorflow #pytorch #developer

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Buddha Community

Deep Learning Frameworks: MxNet vs TensorFlow vs DL4j vs PyTorch
Wissam Muneer

Wissam Muneer

1601433642

Deep Learning Frameworks: MxNet vs TensorFlow vs DL4j vs PyTorch

It’s a great time to be a deep learning engineer. In this article, we will go through some of the popular deep learning frameworks like Tensorflow and CNTK so you can choose which one is best for your project.

Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are neural networks.

Deep learning is the technique of building complex multi-layered neural networks. This helps us solve tough problems like image recognition, language translation, self-driving car technology, and more.

There are tons of real-world applications of deep learning from self-driving Tesla cars to AI assistants like Siri. To build these neural networks, we use different frameworks like Tensorflow, CNTK, and MxNet.

If you are new to deep learning, start here for a good overview.

Frameworks

Without the right framework, constructing quality neural networks can be hard. With the right framework, you only have to worry about getting your hands on the right data.

That doesn’t imply that knowledge of the deep learning frameworks alone is enough to make you a successful data scientist.

You need a strong foundation of the fundamental concepts to be a successful deep learning engineer. But the right framework will make your life easier.

Also, not all programming languages have their own machine learning / deep learning frameworks. This is because not all programming languages have the capacity to handle machine learning problems.

Languages like Python stand out among others due to their complex data processing capability.

Let’s go through some of the popular deep learning frameworks in use today. Each one comes with its own set of advantages and limitations. It is important to have at least a basic understanding of these frameworks so you can choose the right one for your organization or project.

TensorFlow

TensorFlow is the most famous deep learning library around. If you are a data scientist, you probably started with Tensorflow. It is one of the most efficient open-source libraries to work with.

Google built TensorFlow to use as an internal deep learning tool before open-sourcing it. TensorFlow powers a lot of useful applications including Uber, Dropbox, and Airbnb.

Advantages of Tensorflow

  • User Friendly. Easy to learn if you are familiar with Python.
  • Tensorboard for monitoring and visualization. It is a great tool if you want to see your deep learning models in action.
  • Community support. Experts engineers from Google and other companies improve TensorFlow almost on a daily basis.
  • You can use TensorFlow Lite to run TensorFlow models on mobile devices.
  • Tensorflow.js lets you to run real-time deep learning models in the browser using JavaScript.

Limitations of Tensorflow

  • TensorFlow is a bit slow compared to frameworks like MxNet and CNTK.
  • Debugging can be challenging.
  • No support for OpenCL.

#deep-learning #machine-learning #tensorflow #pytorch #developer

Margaret D

Margaret D

1618317562

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.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Rusty  Shanahan

Rusty Shanahan

1596761460

Top Deep Learning Frameworks in 2020: PyTorch vs TensorFlow

Introduction

Deep learning is a sub-branch of machine learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table. When trained with a vast amount of data, Deep Learning systems can match, and even exceed, the cognitive powers of the human brain. How do the two top deep learning frameworks, i.e., PyTorch and TensorFlow, compare?

This article outlines five factors to help you compare these two major deep learning frameworks.

How Do PyTorch and TensorFlow Compare

Ramp-Up Time

Tensorflow is basically a programming language that is embedded within Python, as Sorrow Beaver notes. Tensorflow’s code gets ‘compiled’ into a graph by Python. It is then run by the TensorFlow execution engine. Pytorch, on the other hand, is essentially a GPU enabled drop-in replacement for NumPy that is equipped with a higher-level functionality to build and train deep neural networks.

With Pytorch, the code executes very fast, it is very efficient, and you will require no new concepts to learn. Tensorflow, on the other hand, requires concepts such as placeholders, Variable scoping as well as sessions.

Graph Construction and Debugging

Pytorch has a dynamic process of creating a graph. Graphs on PyTorch can be built by interpreting a line of code corresponding to the particular aspect of a graph.

Tensorflow, on the other hand, has a static process of graph creation that involves graphs going through compilation and running on the execution engine.

Pytorch code will use the standard Python debugger, unlike TensorFlow, where you will need to learn the TF debugger and request the variables from the session for inspection.

Coverage

Tensorflow supports features such as:

  • Fast Fourier transforms
  • Checking a tensor for NaN and infinity
  • Flipping a tensor along a dimension

These are features that Pytorch doesn’t have, but as it grows in popularity, the gap will definitely be bridged.

Serialization

When comparing the two frameworks in serialization, TensorFlow’s graph can be saved as a protocol buffer, which includes operations and parameters. The TensorFlow graph can then be loaded in other programming languages, such as Java and C++. This is important, especially for deployment stacks, where Python is not an option.

Pytorch, on the other hand, has a simple API that can either pickle the entire class or save all weights of a model.

All in all, saving and loading models are simplified in these two frameworks.

#deep learning #artificial inteligence #tensorflow #pytorch #deep learning

How PyTorch Is Challenging TensorFlow Lately

  • PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.

Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground.

Register for Hands-on Workshop (17th Jun) - oneAPI AI Analytics Toolkit

Interest over time for TensorFlow (top) and PyTorch (bottom) in India (Credit: Google Trends)

PyTorch–a framework for deep learning that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing–made some recent additions:

  • Enterprise support**: **After taking over the Windows 10 PyTorch library from Facebook to boost GPU-accelerated machine learning training on Windows 10’s Subsystem for Linux(WSL), Microsoft recently added enterprise support for PyTorch AI on Azure to give PyTorch users a more reliable production experience. “This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments,” Jeremy Jancsary, a senior principal research scientist at Nuance, said.
  • PyTorchVideois a deep learning library for video understanding unveiled by Facebook AI recently. The source code is available on GitHub. With this, Facebook aims to support researchers develop cutting-edge machine learning models and tools. These models can enhance video understanding capabilities along with providing a unified repository of reproducible and efficient video understanding components for research and production applications.
  • PyTorch Profiler: In April this year, PyTorch announced its new performance debug profiler, PyTorch Profiler, along with its 1.8.1 version release. The new tool enables accurate and efficient performance analysis in large scale deep learning models.

#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0

Mikel  Okuneva

Mikel Okuneva

1603735200

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.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020