Hertha  Walsh

Hertha Walsh

1602824400

How To — Ditching Ubuntu in favor of Arch Linux for a Deep Learning Workstation

Why should I ditch Ubuntu?

Most of you might be using Ubuntu for their workstations, and that is fine for the more inexperienced users. One of the issues I had with Ubuntu and the Tensorflow/CUDA though, has been that handling the different drivers and versions of CUDA, cudnn, TensorFlow, and so on has been quite a struggle. I’m not sure about you, but once I had a working Tensorflow 1.15 or 2.0 environment, I usually did not touch it anymore being scared to mess up this holy configuration.

Working with different programs it would be nice to have a way of switching between the two most used TensorFlow versions of 1.15 and 2.0 like you can do with Google Colab in a single command, but installing a different TensorFlow version usually messed up my system again.

Additionally, Arch has always been on my To-Do list, as it is the most “barebone” Linux distro you can get, meaning you are working way closer on the hardware compared to “higher abstractions” like Ubuntu. In their own words, Ubuntu is built to “work out of the box and make the installation process as easy as possible for new users”, whilst the motto of Arch Linux is “customize everything”. Being way closer to the hardware Arch is insanely faster compared to Ubuntu (and miles ahead of Windows), for the cost of more Terminal usage.

When I have been using Arch in the past weeks, RAM usage usually halved compared to Ubuntu, and installing Machine Learning packages is a breeze. I can have both TensorFlow 1.15 and 2.0 working together, switching the versions with Anaconda environments. Also, the system works quite stable, as I am using the LTS (long term support) kernels of Linux, and usually updates to the famous AUR (user-made packages in Arch) are coming out a month ahead of the Debian (Ubuntu) packages.

All in all, I can only recommend setting up an Arch Linux Deep Learning station as it is:

  1. Faster, like packages will install super fast, deep learning is supercharged, …
  2. More stable
  3. Easier to switch between TensorFlow versions compared to Ubuntu.

I will split the how-to in two parts, the first one being “How to I install Arch Linux” and the second one being “How to install the Deep Learning workstation packages”.

For the general “How to install Arch Linux”, head over to this article.

If Arch is too complex for now, you could try out Manjaro, which is a user-friendly version of Arch, even though I can not guarantee that all packages will work the same, as they are slightly different. All in all it should work the same though.

I was thinking about creating a ready to install Image (iso or img), if enough people are interested leave a comment below or message me!

#data-engineering #machine-learning-tools #arch-linux #deep-learning #deep-learning-framework

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How To — Ditching Ubuntu in favor of Arch Linux for a Deep Learning Workstation
Hertha  Walsh

Hertha Walsh

1602824400

How To — Ditching Ubuntu in favor of Arch Linux for a Deep Learning Workstation

Why should I ditch Ubuntu?

Most of you might be using Ubuntu for their workstations, and that is fine for the more inexperienced users. One of the issues I had with Ubuntu and the Tensorflow/CUDA though, has been that handling the different drivers and versions of CUDA, cudnn, TensorFlow, and so on has been quite a struggle. I’m not sure about you, but once I had a working Tensorflow 1.15 or 2.0 environment, I usually did not touch it anymore being scared to mess up this holy configuration.

Working with different programs it would be nice to have a way of switching between the two most used TensorFlow versions of 1.15 and 2.0 like you can do with Google Colab in a single command, but installing a different TensorFlow version usually messed up my system again.

Additionally, Arch has always been on my To-Do list, as it is the most “barebone” Linux distro you can get, meaning you are working way closer on the hardware compared to “higher abstractions” like Ubuntu. In their own words, Ubuntu is built to “work out of the box and make the installation process as easy as possible for new users”, whilst the motto of Arch Linux is “customize everything”. Being way closer to the hardware Arch is insanely faster compared to Ubuntu (and miles ahead of Windows), for the cost of more Terminal usage.

When I have been using Arch in the past weeks, RAM usage usually halved compared to Ubuntu, and installing Machine Learning packages is a breeze. I can have both TensorFlow 1.15 and 2.0 working together, switching the versions with Anaconda environments. Also, the system works quite stable, as I am using the LTS (long term support) kernels of Linux, and usually updates to the famous AUR (user-made packages in Arch) are coming out a month ahead of the Debian (Ubuntu) packages.

All in all, I can only recommend setting up an Arch Linux Deep Learning station as it is:

  1. Faster, like packages will install super fast, deep learning is supercharged, …
  2. More stable
  3. Easier to switch between TensorFlow versions compared to Ubuntu.

I will split the how-to in two parts, the first one being “How to I install Arch Linux” and the second one being “How to install the Deep Learning workstation packages”.

For the general “How to install Arch Linux”, head over to this article.

If Arch is too complex for now, you could try out Manjaro, which is a user-friendly version of Arch, even though I can not guarantee that all packages will work the same, as they are slightly different. All in all it should work the same though.

I was thinking about creating a ready to install Image (iso or img), if enough people are interested leave a comment below or message me!

#data-engineering #machine-learning-tools #arch-linux #deep-learning #deep-learning-framework

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

Chet  Lubowitz

Chet Lubowitz

1595429220

How to Install Microsoft Teams on Ubuntu 20.04

Microsoft Teams is a communication platform used for Chat, Calling, Meetings, and Collaboration. Generally, it is used by companies and individuals working on projects. However, Microsoft Teams is available for macOS, Windows, and Linux operating systems available now.

In this tutorial, we will show you how to install Microsoft Teams on Ubuntu 20.04 machine. By default, Microsoft Teams package is not available in the Ubuntu default repository. However we will show you 2 methods to install Teams by downloading the Debian package from their official website, or by adding the Microsoft repository.

Install Microsoft Teams on Ubuntu 20.04

1./ Install Microsoft Teams using Debian installer file

01- First, navigate to teams app downloads page and grab the Debian binary installer. You can simply obtain the URL and pull the binary using wget;

$ VERSION=1.3.00.5153
$ wget https://packages.microsoft.com/repos/ms-teams/pool/main/t/teams/teams_${VERSION}_amd64.deb

#linux #ubuntu #install microsoft teams on ubuntu #install teams ubuntu #microsoft teams #teams #teams download ubuntu #teams install ubuntu #ubuntu install microsoft teams #uninstall teams ubuntu

Few Shot Learning — A Case Study (2)

In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.

Please watch the GitHub repository to check out the implementations and keep updated with further experiments.

Introduction to Few-Shot Classification

In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.

In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.

And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.

About Relation Network

The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.

That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.

#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning