Create a Deep Learning Library in JavaScript from Scratch

Building a word2vec model with our deep learning library in JavaScript

Welcome to the fifth part of out series, where we’ve been building a deep learning library in Javascript that mimics the core functions of popular frameworks like TensorFlow and PyTorch.

In the previous parts of the series, we’ve been implementing some basic functions needed to do this.

In the first part, we explored implementing an automatic gradient in JavaScript and also learned how to create some basic maths operations in JavaScript.

In the second part, we dove deep into implementing some of the core parts in building a neural network, such as tensors, linear layers, and ReLU and softmax activation functions.

In the part three, we discussed how to create a Sequential model, implement stochastic gradient optimization, and also implement cross-entropy loss.

And in the fourth part, we implemented visualization of our neural network graph.

In this part, we will be putting a lot of these pieces together to build a word2vec model using our library.

Goals

  • Text pre-processing and cleaning (including skip-gram)
  • Create a neural network for the word2vec model
  • Create a function to obtain the word2vec of a word

#heartbeat #deep-learning #tensorflow #javascript #pytorch

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Create a Deep Learning Library in JavaScript from Scratch
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

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.


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Myah  Conn

Myah Conn

1593292440

Emojify - Create your own emoji with Deep Learning

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.

create emoji with deep learning

About the Dataset

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:

  • 0:angry
  • 1:disgust
  • 2:feat
  • 3:happy
  • 4:sad
  • 5:surprise
  • 6:natural

Download Dataset: Facial Expression Recognition Dataset

Download Project Code

Before proceeding ahead, please download the source code: Emoji Creator Project Source Code

Create your emoji with Deep Learning

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.

Facial Emotion Recognition using CNN

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

Create a Deep Learning Library in JavaScript from Scratch

Welcome to the third part of this series on building deep learning library in JavaScript from scratch.

In the first part of the series, we talked about the basic building blocks of all deep learning libraries—automatic gradients. Additionally, we showed how to implement autograd calculation in JavaScript.

In the second part of the series, We implemented tensors, math operations (add and matmul), a linear layer, and ReLU and softmax activation functions—put together, this gave us a very simple but working neural network.

In this part, we’ll pick up where we left off and implement the following;

  • Sequential model
  • Cross entropy loss
  • Optimization

Sequential Model

As a deep learning practitioner, I’ll again assume you’re familiar to some extent with Sequential models, both in Keras and TensorFlow. But for a quick reminder, you can check out the Keras sequential model here.

In the previous part of the series, we implemented a simple neural network:

let linear1 =    new Linear(2,3)
	let relu =    new ReLU()
	let linear2 =   new Linear(3,2)
	let softmax =    new Softmax()

	var x = new Tensor(1,2,require_grad=true)
	x.setFrom([2,3]);

	// forward pass
	input = linear1.forward(x)
	input = relu.forward(input)
	input =  linear2.forward(input)
	output = softmax.forward(input);

	console.log(output.out) // output = [ 0.5025667023096377, 0.4974332976903622 ]
view raw
NeuralNet.js hosted with ❤ by GitHub

Now, we’ll convert this model into a Sequential model that consists of different numbers of neural layers combined together to form a single model. The Sequential model has more to it than that—but we’ll be discussing that a bit later.

#heartbeat #tensorflow #machine-learning #deep-learning #data-science #deep learning

Sival Alethea

Sival Alethea

1624298400

Learn JavaScript - Full Course for Beginners. DO NOT MISS!!!

This complete 134-part JavaScript tutorial for beginners will teach you everything you need to know to get started with the JavaScript programming language.
⭐️Course Contents⭐️
0:00:00 Introduction
0:01:24 Running JavaScript
0:04:23 Comment Your Code
0:05:56 Declare Variables
0:06:15 Storing Values with the Assignment Operator
0:11:31 Initializing Variables with the Assignment Operator
0:11:58 Uninitialized Variables
0:12:40 Case Sensitivity in Variables
0:14:05 Add Two Numbers
0:14:34 Subtract One Number from Another
0:14:52 Multiply Two Numbers
0:15:12 Dividing Numbers
0:15:30 Increment
0:15:58 Decrement
0:16:22 Decimal Numbers
0:16:48 Multiply Two Decimals
0:17:18 Divide Decimals
0:17:33 Finding a Remainder
0:18:22 Augmented Addition
0:19:22 Augmented Subtraction
0:20:18 Augmented Multiplication
0:20:51 Augmented Division
0:21:19 Declare String Variables
0:22:01 Escaping Literal Quotes
0:23:44 Quoting Strings with Single Quotes
0:25:18 Escape Sequences
0:26:46 Plus Operator
0:27:49 Plus Equals Operator
0:29:01 Constructing Strings with Variables
0:30:14 Appending Variables to Strings
0:31:11 Length of a String
0:32:01 Bracket Notation
0:33:27 Understand String Immutability
0:34:23 Find the Nth Character
0:34:51 Find the Last Character
0:35:48 Find the Nth-to-Last Character
0:36:28 Word Blanks
0:40:44 Arrays
0:41:43 Nest Arrays
0:42:33 Access Array Data
0:43:34 Modify Array Data
0:44:48 Access Multi-Dimensional Arrays
0:46:30 push()
0:47:29 pop()
0:48:33 shift()
0:49:23 unshift()
0:50:36 Shopping List
0:51:41 Write Reusable with Functions
0:53:41 Arguments
0:55:43 Global Scope
0:59:31 Local Scope
1:00:46 Global vs Local Scope in Functions
1:02:40 Return a Value from a Function
1:03:55 Undefined Value returned
1:04:52 Assignment with a Returned Value
1:05:52 Stand in Line
1:08:41 Boolean Values
1:09:24 If Statements
1:11:51 Equality Operator
1:13:18 Strict Equality Operator
1:14:43 Comparing different values
1:15:38 Inequality Operator
1:16:20 Strict Inequality Operator
1:17:05 Greater Than Operator
1:17:39 Greater Than Or Equal To Operator
1:18:09 Less Than Operator
1:18:44 Less Than Or Equal To Operator
1:19:17 And Operator
1:20:41 Or Operator
1:21:37 Else Statements
1:22:27 Else If Statements
1:23:30 Logical Order in If Else Statements
1:24:45 Chaining If Else Statements
1:27:45 Golf Code
1:32:15 Switch Statements
1:35:46 Default Option in Switch Statements
1:37:23 Identical Options in Switch Statements
1:39:20 Replacing If Else Chains with Switch
1:41:11 Returning Boolean Values from Functions
1:42:20 Return Early Pattern for Functions
1:43:38 Counting Cards
1:49:11 Build Objects
1:50:46 Dot Notation
1:51:33 Bracket Notation
1:52:47 Variables
1:53:34 Updating Object Properties
1:54:30 Add New Properties to Object
1:55:19 Delete Properties from Object
1:55:54 Objects for Lookups
1:57:43 Testing Objects for Properties
1:59:15 Manipulating Complex Objects
2:01:00 Nested Objects
2:01:53 Nested Arrays
2:03:06 Record Collection
2:10:15 While Loops
2:11:35 For Loops
2:13:56 Odd Numbers With a For Loop
2:15:28 Count Backwards With a For Loop
2:17:08 Iterate Through an Array with a For Loop
2:19:43 Nesting For Loops
2:22:45 Do…While Loops
2:24:12 Profile Lookup
2:28:18 Random Fractions
2:28:54 Random Whole Numbers
2:30:21 Random Whole Numbers within a Range
2:31:46 parseInt Function
2:32:36 parseInt Function with a Radix
2:33:29 Ternary Operator
2:34:57 Multiple Ternary Operators
2:36:57 var vs let
2:39:02 var vs let scopes
2:41:32 const Keyword
2:43:40 Mutate an Array Declared with const
2:44:52 Prevent Object Mutation
2:47:17 Arrow Functions
2:28:24 Arrow Functions with Parameters
2:49:27 Higher Order Arrow Functions
2:53:04 Default Parameters
2:54:00 Rest Operator
2:55:31 Spread Operator
2:57:18 Destructuring Assignment: Objects
3:00:18 Destructuring Assignment: Nested Objects
3:01:55 Destructuring Assignment: Arrays
3:03:40 Destructuring Assignment with Rest Operator to Reassign Array
3:05:05 Destructuring Assignment to Pass an Object
3:06:39 Template Literals
3:10:43 Simple Fields
3:12:24 Declarative Functions
3:12:56 class Syntax
3:15:11 getters and setters
3:20:25 import vs require
3:22:33 export
3:23:40 * to Import
3:24:50 export default
3:25:26 Import a Default Export
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=PkZNo7MFNFg&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=4

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