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Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
#deep-learning #data-science #r-programming
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A cross-platform command line REPL for the rapid experimentation and exploration of C#. It supports intellisense, installing NuGet packages, and referencing local .NET projects and assemblies.
(click to view animation)
C# REPL provides the following features:
C# REPL is a .NET 6 global tool, and runs on Windows 10, Mac OS, and Linux. It can be installed via:
dotnet tool install -g csharprepl
If you're running on Mac OS Catalina (10.15) or later, make sure you follow any additional directions printed to the screen. You may need to update your PATH variable in order to use .NET global tools.
After installation is complete, run csharprepl
to begin. C# REPL can be updated via dotnet tool update -g csharprepl
.
Run csharprepl
from the command line to begin an interactive session. The default colorscheme uses the color palette defined by your terminal, but these colors can be changed using a theme.json
file provided as a command line argument.
Type some C# into the prompt and press Enter to run it. The result, if any, will be printed:
> Console.WriteLine("Hello World")
Hello World
> DateTime.Now.AddDays(8)
[6/7/2021 5:13:00 PM]
To evaluate multiple lines of code, use Shift+Enter to insert a newline:
> var x = 5;
var y = 8;
x * y
40
Additionally, if the statement is not a "complete statement" a newline will automatically be inserted when Enter is pressed. For example, in the below code, the first line is not a syntactically complete statement, so when we press enter we'll go down to a new line:
> if (x == 5)
| // caret position, after we press Enter on Line 1
Finally, pressing Ctrl+Enter will show a "detailed view" of the result. For example, for the DateTime.Now
expression below, on the first line we pressed Enter, and on the second line we pressed Ctrl+Enter to view more detailed output:
> DateTime.Now // Pressing Enter shows a reasonable representation
[5/30/2021 5:13:00 PM]
> DateTime.Now // Pressing Ctrl+Enter shows a detailed representation
[5/30/2021 5:13:00 PM] {
Date: [5/30/2021 12:00:00 AM],
Day: 30,
DayOfWeek: Sunday,
DayOfYear: 150,
Hour: 17,
InternalKind: 9223372036854775808,
InternalTicks: 637579915804530992,
Kind: Local,
Millisecond: 453,
Minute: 13,
Month: 5,
Second: 0,
Ticks: 637579915804530992,
TimeOfDay: [17:13:00.4530992],
Year: 2021,
_dateData: 9860951952659306800
}
A note on semicolons: C# expressions do not require semicolons, but statements do. If a statement is missing a required semicolon, a newline will be added instead of trying to run the syntatically incomplete statement; simply type the semicolon to complete the statement.
> var now = DateTime.Now; // assignment statement, semicolon required
> DateTime.Now.AddDays(8) // expression, we don't need a semicolon
[6/7/2021 5:03:05 PM]
Use the #r
command to add assembly or nuget references.
#r "AssemblyName"
or #r "path/to/assembly.dll"
#r "path/to/project.csproj"
. Solution files (.sln) can also be referenced.#r "nuget: PackageName"
to install the latest version of a package, or #r "nuget: PackageName, 13.0.5"
to install a specific version (13.0.5 in this case).To run ASP.NET applications inside the REPL, start the csharprepl
application with the --framework
parameter, specifying the Microsoft.AspNetCore.App
shared framework. Then, use the above #r
command to reference the application DLL. See the Command Line Configuration section below for more details.
csharprepl --framework Microsoft.AspNetCore.App
The C# REPL supports multiple configuration flags to control startup, behavior, and appearance:
csharprepl [OPTIONS] [response-file.rsp] [script-file.csx] [-- <additional-arguments>]
Supported options are:
-r <dll>
or --reference <dll>
: Reference an assembly, project file, or nuget package. Can be specified multiple times. Uses the same syntax as #r
statements inside the REPL. For example, csharprepl -r "nuget:Newtonsoft.Json" "path/to/myproj.csproj"
-u <namespace>
or --using <namespace>
: Add a using statement. Can be specified multiple times.-f <framework>
or --framework <framework>
: Reference a shared framework. The available shared frameworks depends on the local .NET installation, and can be useful when running an ASP.NET application from the REPL. Example frameworks are:-t <theme.json>
or --theme <theme.json>
: Read a theme file for syntax highlighting. This theme file associates C# syntax classifications with colors. The color values can be full RGB, or ANSI color names (defined in your terminal's theme). The NO_COLOR standard is supported.--trace
: Produce a trace file in the current directory that logs CSharpRepl internals. Useful for CSharpRepl bug reports.-v
or --version
: Show version number and exit.-h
or --help
: Show help and exit.response-file.rsp
: A filepath of an .rsp file, containing any of the above command line options.script-file.csx
: A filepath of a .csx file, containing lines of C# to evaluate before starting the REPL. Arguments to this script can be passed as <additional-arguments>
, after a double hyphen (--
), and will be available in a global args
variable.If you have dotnet-suggest
enabled, all options can be tab-completed, including values provided to --framework
and .NET namespaces provided to --using
.
C# REPL is a standalone software application, but it can be useful to integrate it with other developer tools:
To add C# REPL as a menu entry in Windows Terminal, add the following profile to Windows Terminal's settings.json
configuration file (under the JSON property profiles.list
):
{
"name": "C# REPL",
"commandline": "csharprepl"
},
To get the exact colors shown in the screenshots in this README, install the Windows Terminal Dracula theme.
To use the C# REPL with Visual Studio Code, simply run the csharprepl
command in the Visual Studio Code terminal. To send commands to the REPL, use the built-in Terminal: Run Selected Text In Active Terminal
command from the Command Palette (workbench.action.terminal.runSelectedText
).
To add the C# REPL to the Windows Start Menu for quick access, you can run the following PowerShell command, which will start C# REPL in Windows Terminal:
$shell = New-Object -ComObject WScript.Shell
$shortcut = $shell.CreateShortcut("$env:appdata\Microsoft\Windows\Start Menu\Programs\csharprepl.lnk")
$shortcut.TargetPath = "wt.exe"
$shortcut.Arguments = "-w 0 nt csharprepl.exe"
$shortcut.Save()
You may also wish to add a shorter alias for C# REPL, which can be done by creating a .cmd
file somewhere on your path. For example, put the following contents in C:\Users\username\.dotnet\tools\csr.cmd
:
wt -w 0 nt csharprepl
This will allow you to launch C# REPL by running csr
from anywhere that accepts Windows commands, like the Window Run dialog.
This project is far from being the first REPL for C#. Here are some other projects; if this project doesn't suit you, another one might!
Visual Studio's C# Interactive pane is full-featured (it has syntax highlighting and intellisense) and is part of Visual Studio. This deep integration with Visual Studio is both a benefit from a workflow perspective, and a drawback as it's not cross-platform. As far as I know, the C# Interactive pane does not support NuGet packages or navigating to documentation/source code. Subjectively, it does not follow typical command line keybindings, so can feel a bit foreign.
csi.exe ships with C# and is a command line REPL. It's great because it's a cross platform REPL that comes out of the box, but it doesn't support syntax highlighting or autocompletion.
dotnet script allows you to run C# scripts from the command line. It has a REPL built-in, but the predominant focus seems to be as a script runner. It's a great tool, though, and has a strong community following.
dotnet interactive is a tool from Microsoft that creates a Jupyter notebook for C#, runnable through Visual Studio Code. It also provides a general framework useful for running REPLs.
Download Details:
Author: waf
Source Code: https://github.com/waf/CSharpRepl
License: MPL-2.0 License
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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
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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
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.
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.
#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
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Deep Learning project for beginners – Taking you closer to your Data Science dream
Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also lead to increasing data science research dedicated to emoji-driven storytelling.
With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. In this deep learning project, we will classify human facial expressions to filter and map corresponding emojis or avatars.
The FER2013 dataset ( facial expression recognition) consists of 48*48 pixel grayscale face images. The images are centered and occupy an equal amount of space. This dataset consist of facial emotions of following categories:
Download Dataset: Facial Expression Recognition Dataset
Before proceeding ahead, please download the source code: Emoji Creator Project Source Code
We will build a deep learning model to classify facial expressions from the images. Then we will map the classified emotion to an emoji or an avatar.
In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images.
Download the dataset from the above link. Extract it in the data folder with separate train and test directories.
#python tutorials #create emoji with deep learning #deep learning project #deep learning project for beginners #deep learning project with source code
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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:
Training/Testing procedure:
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