Dejah  Reinger

Dejah Reinger

1601378640

Setting up your PC/Workstation for Deep Learning: Tensorflow and PyTorch — Windows

This article will guide you through the whole process of setting up the required tools and installing drivers required for Deep Learning on your windows machine. Surprisingly, even setting up the environment for doing Deep Learning isn’t that easy. Chances of you breaking something during this process is actually pretty high. I have experienced setting up everything required for Deep Learning from scratch quite a few times, albeit in a different more programmer-friendly OS in Linux. (a guide on that is next in line)

There are very few articles explaining the same process for Windows at the moment. So I decided to give it a shot. Recently, after breaking things a few times, I finally found a proper solution to this problem. Not only this method results in a successful setup but it is also much easier than what I’ve seen most others do.

#tutorial #deep-learning #tensorflow #machine-learning #pytorch

What is GEEK

Buddha Community

Setting up your PC/Workstation for Deep Learning: Tensorflow and PyTorch — Windows
Dejah  Reinger

Dejah Reinger

1601378640

Setting up your PC/Workstation for Deep Learning: Tensorflow and PyTorch — Windows

This article will guide you through the whole process of setting up the required tools and installing drivers required for Deep Learning on your windows machine. Surprisingly, even setting up the environment for doing Deep Learning isn’t that easy. Chances of you breaking something during this process is actually pretty high. I have experienced setting up everything required for Deep Learning from scratch quite a few times, albeit in a different more programmer-friendly OS in Linux. (a guide on that is next in line)

There are very few articles explaining the same process for Windows at the moment. So I decided to give it a shot. Recently, after breaking things a few times, I finally found a proper solution to this problem. Not only this method results in a successful setup but it is also much easier than what I’ve seen most others do.

#tutorial #deep-learning #tensorflow #machine-learning #pytorch

Marget D

Marget 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

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

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

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