Google Colab is a widely popular cloud service for machine learning that features free access to GPU and TPU computing. Follow this detailed guide to help you get up and running fast to develop your next deep learning algorithms with Colab.
Google Colab is one of the most famous cloud services for seasoned data scientists, researchers, and software engineers. While Google Colab seems easy to start, some things are difficult to use. In this guide, you will learn:
Creating a new notebook
Import Notebooks from GitHub/local machine
Google Drive with Colab
Keyboard shortcuts for Colab
Change Language (Python 3 -> Python 2)
Select GPU or TPU
Load Data from Drive
Load Data from Github Repository
Importing External Datasets such as from Kaggle
Bash commands in Colab
#2020 jun tutorials # overviews #uncategorized #deep learning #github #google colab #gpu #jupyter
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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
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:
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.
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.
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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Not everyone can indeed afford a powerful computing system. We have nothing but to adjust with less computing system which runs code for hours and even days. Most of us have got a system with RAM 4 GB or 8 GB and only a few have RAM beyond 8 GB with an advanced processor. What can we do if we have a less powerful computing system? Should we build-up patience and wait for the code to run for hours and even days? No, we don’t have to wait for hours or days to run certain codes. Here comes rescuer — Google Colab. Google Colab is also known as Google Colaboratory. Google Colab is a jupyter notebook environment. It is a free source provided by google wherein we can write and execute code. We can use Google Colab with ease just as we use local jupyter. Google Colab provides RAM of 12 GB with a maximum extension of 25 GB and a disk space of 358.27 GB. Wow! It is great to have a free source with such huge RAM and disk space.
RAM and Disk Space provided by Google Colab
FYI: If certain code takes 1 hour to run in local Jupyter or Spyder or any other environment, same code takes around 10–15 minutes to run in Google Colab. Amazing, isn’t it!
Well, I must admit that it is not that tough to learn to use Google Colab. We can easily learn to use it in the first attempt. Click on the Google Colab link: https://colab.research.google.com/ which navigates us to the official site. This is how it looks when we click on the above link.
Google Colab Official Site View
In the above image, we can see a dialog box with headings** ‘Examples’, ‘Recent’, ‘Google Drive’, ‘Github’, and ‘Upload’.** Each heading is nothing but sections through which we can open our notebook and use it for further analysis.
Under this section, we can explore google colab like overview, guide, etc.
Under this section, only those notebooks will be displayed which are recently used. You can directly go to the Recent section and open a recent notebook for further analysis.
Under this section, all the notebooks which are in our google drive are displayed. We can access it by click on it and use for further analysis.
Under this section, all the notebooks which are in our Github are displayed. We just need to paste the respective link and get access to notebooks.
Under this section, we can upload notebooks from our local drive and access it.
If we want to create a new notebook, then we should click on Cancel button (Bottom Right) in the dialog box. Below is the image for reference.
#data-science #deep-learning #google-drive #machine-learning #google-colab #deep learning
The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.
Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.
The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.
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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:
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.
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.
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.
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:
We can define the whole procedure in just 5 steps.
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