AI Community AI Community
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: pub@towardsai.net
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

Publication

The NLP Cypher | 03.21.21
Latest   Machine Learning   Newsletter

The NLP Cypher | 03.21.21

ifttt-user
0 like
July 20, 2023
Share this post

Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Thorgerson

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 03.21.21

The Field of Reeds

Hey, welcome back! Let’s kick off the newsletter with a lucid sonic dream from a very crafty GAN U+1F635U+1F344. But wait, what is that exactly? For direct experience check out the video below. TL;DR: a savvy dev trained a GAN to generate acid-trip art that transitions in sync with music. This isn’t NLP related but hey, still cool! Here’s the library, it runs on TF-v1, now go get a wav file and play some Pink Floyd:

U+1F344

mikaelalafriz/lucid-sonic-dreams

Lucid Sonic Dreams syncs GAN-generated visuals to music. By default, it uses NVLabs StyleGAN2, with pre-trained models…

github.com

Surprise Colab:

Google Colaboratory

Edit description

colab.research.google.com

If you enjoy this read, please give it a U+1F44FU+1F44F and share with friends… U+1F60E

TensorFlow Transformers Library U+1F440

pip install tf-transformers

Just when you thought you’ve seen it all, a library comes along that augments the capability of TensorFlow v2 focusing on NLP. The authors make impressive claims, here are the highlights (from their repo):

  • Faster Auto Regressive Decoding using Tensorflow2. Faster than PyTorch in most experiments (V100 GPU). 80% faster compared to existing TF based libraries (relative difference) Refer benchmark code.
  • Complete TFlite support for BERT, RoBERTA, T5, Albert, mt5 for all down stream tasks except text-generation.
  • Faster sentence-piece alignment (no more LCS overhead).
  • Variable batch text generation for Encoder only models like GPT2.
  • No more hassle of writing long codes for TFRecords. minimal and simple.
  • Off the shelf support for auto-batching tf.data.dataset or tf.ragged tensors.
  • Pass dictionary outputs directly to loss functions inside tf.keras.Model.fit using model.compile2 . Refer examples or blog.
  • Multiple mask modes like causal, user-defined, prefix by changing one argument . Refer examples or blog.

Model Support: ALBERT, BERT, RoBERTa, GPT-2, MT5, ELECTRA and T5.

They even include code to switch from Hugging Face to their library: MORTAL KOMBAT U+1F976…

legacyai/tf-transformers

State of the art faster Natural Language Processing in Tensorflow 2.0 . – legacyai/tf-transformers

github.com

GitHub

legacyai/tf-transformers

tf-transformers: faster and easier state-of-the-art NLP in TensorFlow 2.0 tf-transformers is designed to harness the…

github.com

C4: 800GB of English Text Released Into the Wild

Google’s C4 dataset has been in a full quarantine. They never offered the option to download it, we could only replicate it. However AllenNLP came along to save the day, and they brought receipts.

They have 3 variants:

  • en: 800GB in TFDS format, 300GB in JSON format
  • en.noclean: 6.3TB in TFDS format, 2.3TB in JSON format
  • realnewslike: 38GB in TFDS format, 15GB in JSON format

Download the C4 dataset! · Discussion #5056 · allenai/allennlp

You can't perform that action at this time. You signed in with another tab or window. You signed out in another tab or…

github.com

Multi-Lingual CLIP

How about a multi-lingual CLIP?

How about 101 source languages using multi-lingual BERT (distilled or base)?

This library offers this, and includes an intuitive Colab to test inference. U+1F525U+1F525

Feel free to contact the authors if you want add a language that is currently not supported. This is great work.

FreddeFrallan/Multilingual-CLIP

Colab Notebook · Pre-trained Models · Report Bug OpenAI recently released the paper Learning Transferable Visual Models…

github.com

Almond V.2 Stanford’s Open Sourced Voice Assistant

Want to help develop an open-sourced voice assistant? Almond is here:

Features:

  • Spotify (music)
  • Home Assistant (IoT)
  • Weather
  • Jokes
  • Local restaurants
  • FAQs about the assistant itself

Call for testing: Almond 2.0 Alpha

Hello all! I am very pleased to announce the immediate availability of the new alpha release of Almond. This is the…

community.almond.stanford.edu

A100s Bonanza

Google Cloud Platform update, if you want to go nuts with distributed computing you can now get 16 A100s on a single A2 instance. This is serious horsepower, if you want to know what the upper bound of compute looks like on GCP, this is it.

A2 VMs with NVIDIA A100 GPUs are GA U+007C Google Cloud Blog

A2 VMs with NVIDIA A100 GPUs are now generally available for your most demanding workloads including machine learning…

cloud.google.com

Getting Started with Rust

It’s like Python but fast U+1F601. If you want to know the current state of Rust, check out the Stack Overflow blog U+1F447 . It highlights how the small but devoted Rust community continues to show strength and includes links to tutorials.

Getting started with … Rust – Stack Overflow Blog

In this series, we look at the most loved languages according to the Stack Overflow developer survey, the spread and…

stackoverflow.blog

TensorFlow and Quantum: 1 Year Through the Looking Glass

An overview of the Quantum TF library found here:

tensorflow/quantum

TensorFlow Quantum (TFQ) is a Python framework for hybrid quantum-classical machine learning that is primarily focused…

github.com

Blog

TensorFlow Quantum turns one year old

March 18, 2021 — Posted by Michael Broughton, Alan Ho, Masoud Mohseni Last year we announced TensorFlow Quantum (TFQ)…

blog.tensorflow.org

Software Updates U+1F4BB

Sentence Transformers Update

declassified

Transformers 4.4.2

Releases · huggingface/transformers

Two new models are released as part of the S2T implementation: Speech2TextModel and…

github.com

AresDB — GPU Powered Real-Time Storage and Query Engine (in GoLang)

From Uber’s engineering group, this is the source code for AresDB. You get to hook up GPUs for super low latency database querying.

uber/aresdb

AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data…

github.com

Original release blog:

Introducing AresDB: Uber's GPU-Powered Open Source, Real-time Analytics Engine

At Uber, real-time analytics allow us to attain business insights and operational efficiency, enabling us to make…

eng.uber.com

SpeechBrain

An awesome new speech library with several pretrained models on the HF repo. Tasks it supports: speaker recognition, speech identification and speech diarization. It runs on PyTorch.

SpeechBrain: A PyTorch Speech Toolkit

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and…

speechbrain.github.io

MongoDB for Storing and Retrieving ML Models

Tutorial w/ code:

How to Use MongoDB to Store and Retrieve ML Models — Python Simplified

If you are looking for a database for storing your machine learning models then this article is for you. You … How to…

pythonsimplified.com

The Mega (Incredible) PyTorch Repo

“This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.”

This is a HUGE index.

ritchieng/the-incredible-pytorch

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible…

github.com

Repo Cypher U+1F468‍U+1F4BB

A collection of recently released repos that caught our U+1F441

CARTON Transformer

aka: Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs…

Performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph.

endrikacupaj/CARTON

Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs…

github.com

Connected Papers U+1F4C8

Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling

Train a joint intent prediction and slot filling model in English and generalize to other languages.

jitinkrishnan/Multilingual-ZeroShot-SlotFilling

Goal: Train a joint intent prediction and slot fillinf model using English and generalize to other languages…

github.com

Connected Papers U+1F4C8

BERTRL: Inductive Relation Prediction by BERT

Using BERT to do knowledge base completion.

zhw12/BERTRL

Code and Data for Paper [Inductive Relation Prediction by BERT], this paper proposes an algorithm to do knowledge base…

github.com

Connected Papers U+1F4C8

Multimodal End-to-End Sparse Model for Emotion Recognition

wenliangdai/Multimodal-End2end-Sparse

Paper accepted at the NAACL 2021: Multimodal End-to-End Sparse Model for Emotion Recognition, by Wenliang Dai * …

github.com

Connected Papers U+1F4C8

VitaminC

Using Transformers to fine-tune the VitaminC dataset for fact verification.

TalSchuster/VitaminC

This repository contains the dataset and model for the NAACL 2021 paper: Get Your Vitamin C! Robust Fact Verification…

github.com

Connected Papers U+1F4C8

Enconter

ENtity-CONstrained insertion TransformER, a language model to help improve fine control of content generation i.e. a method for dealing with entity constraints.

LARC-CMU-SMU/Enconter

Implementation of 2021 EACL paper Enconter. Contribute to LARC-CMU-SMU/Enconter development by creating an account on…

github.com

Connected Papers U+1F4C8

Dataset of the Week: ParaQA

What is it?

A question answering dataset with paraphrase responses for single-turn conversation.

Dataset contains 5,000 question-answer pairs with a minimum of two and a maximum of eight unique paraphrased responses for each question.

Sample

Where is it?

barshana-banerjee/ParaQA

The dataset was created using a semi-automated framework for generating diverse paraphrasing of the answers using…

github.com

Every Sunday we do a weekly round-up of NLP news and code drops from researchers around the world.

For complete coverage, follow our Twitter: @Quantum_Stat

Quantum Stat

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

, , , , ,
Share this post

Related posts

LAI #66: Information Theory for People in a Hurry
Artificial Intelligence, Latest, Machine Learning

LAI #66: Information Theory for People in a Hurry

Towards AI Editorial Team
0 like
WHAT IS MCP BY ANTHROPIC?(MODEL CONTEXT PROTOCOL)
Data Science, Latest, Machine Learning

WHAT IS MCP BY ANTHROPIC?(MODEL CONTEXT PROTOCOL)

Adarsh Menon
0 like
FactoryBERT: An AI That Understands Manufacturing
Data Science, Latest, Machine Learning

FactoryBERT: An AI That Understands Manufacturing

Saif Ali Kheraj
1 like
🔎 Decoding LLM Pipeline — Step 1: Input Processing & Tokenization
Artificial Intelligence, Latest, Machine Learning

🔎 Decoding LLM Pipeline — Step 1: Input Processing & Tokenization

Ecem Karaman
0 like

Feedback ↓

Sign Up for the Course
`; } else { console.error('Element with id="subscribe" not found within the page with class "home".'); } } }); // Remove duplicate text from articles /* Backup: 09/11/24 function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag elements.forEach(el => { const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 2) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); */ // Remove duplicate text from articles function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag // List of classes to be excluded const excludedClasses = ['medium-author', 'post-widget-title']; elements.forEach(el => { // Skip elements with any of the excluded classes if (excludedClasses.some(cls => el.classList.contains(cls))) { return; // Skip this element if it has any of the excluded classes } const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 10) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); //Remove unnecessary text in blog excerpts document.querySelectorAll('.blog p').forEach(function(paragraph) { // Replace the unwanted text pattern for each paragraph paragraph.innerHTML = paragraph.innerHTML .replace(/Author\(s\): [\w\s]+ Originally published on Towards AI\.?/g, '') // Removes 'Author(s): XYZ Originally published on Towards AI' .replace(/This member-only story is on us\. Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

Subscribe to our AI newsletter!

' + */ '

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

'+ '

Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

' + '
' + '' + '' + '

Note: Content contains the views of the contributing authors and not Towards AI.
Disclosure: This website may contain sponsored content and affiliate links.

' + 'Discover Your Dream AI Career at Towards AI Jobs' + '

Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 10,000 live jobs today with Towards AI Jobs!

' + '
' + '

🔥 Recommended Articles 🔥

' + 'Why Become an LLM Developer? Launching Towards AI’s New One-Stop Conversion Course'+ 'Testing Launchpad.sh: A Container-based GPU Cloud for Inference and Fine-tuning'+ 'The Top 13 AI-Powered CRM Platforms
' + 'Top 11 AI Call Center Software for 2024
' + 'Learn Prompting 101—Prompt Engineering Course
' + 'Explore Leading Cloud Providers for GPU-Powered LLM Training
' + 'Best AI Communities for Artificial Intelligence Enthusiasts
' + 'Best Workstations for Deep Learning
' + 'Best Laptops for Deep Learning
' + 'Best Machine Learning Books
' + 'Machine Learning Algorithms
' + 'Neural Networks Tutorial
' + 'Best Public Datasets for Machine Learning
' + 'Neural Network Types
' + 'NLP Tutorial
' + 'Best Data Science Books
' + 'Monte Carlo Simulation Tutorial
' + 'Recommender System Tutorial
' + 'Linear Algebra for Deep Learning Tutorial
' + 'Google Colab Introduction
' + 'Decision Trees in Machine Learning
' + 'Principal Component Analysis (PCA) Tutorial
' + 'Linear Regression from Zero to Hero
'+ '
', /* + '

Join thousands of data leaders on the AI newsletter. It’s free, we don’t spam, and we never share your email address. Keep up to date with the latest work in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

',*/ ]; var replaceText = { '': '', '': '', '
': '
' + ctaLinks + '
', }; Object.keys(replaceText).forEach((txtorig) => { //txtorig is the key in replacetext object const txtnew = replaceText[txtorig]; //txtnew is the value of the key in replacetext object let entryFooter = document.querySelector('article .entry-footer'); if (document.querySelectorAll('.single-post').length > 0) { //console.log('Article found.'); const text = entryFooter.innerHTML; entryFooter.innerHTML = text.replace(txtorig, txtnew); } else { // console.log('Article not found.'); //removing comment 09/04/24 } }); var css = document.createElement('style'); css.type = 'text/css'; css.innerHTML = '.post-tags { display:none !important } .article-cta a { font-size: 18px; }'; document.body.appendChild(css); //Extra //This function adds some accessibility needs to the site. function addAlly() { // In this function JQuery is replaced with vanilla javascript functions const imgCont = document.querySelector('.uw-imgcont'); imgCont.setAttribute('aria-label', 'AI news, latest developments'); imgCont.title = 'AI news, latest developments'; imgCont.rel = 'noopener'; document.querySelector('.page-mobile-menu-logo a').title = 'Towards AI Home'; document.querySelector('a.social-link').rel = 'noopener'; document.querySelector('a.uw-text').rel = 'noopener'; document.querySelector('a.uw-w-branding').rel = 'noopener'; document.querySelector('.blog h2.heading').innerHTML = 'Publication'; const popupSearch = document.querySelector$('a.btn-open-popup-search'); popupSearch.setAttribute('role', 'button'); popupSearch.title = 'Search'; const searchClose = document.querySelector('a.popup-search-close'); searchClose.setAttribute('role', 'button'); searchClose.title = 'Close search page'; // document // .querySelector('a.btn-open-popup-search') // .setAttribute( // 'href', // 'https://medium.com/towards-artificial-intelligence/search' // ); } // Add external attributes to 302 sticky and editorial links function extLink() { // Sticky 302 links, this fuction opens the link we send to Medium on a new tab and adds a "noopener" rel to them var stickyLinks = document.querySelectorAll('.grid-item.sticky a'); for (var i = 0; i < stickyLinks.length; i++) { /* stickyLinks[i].setAttribute('target', '_blank'); stickyLinks[i].setAttribute('rel', 'noopener'); */ } // Editorial 302 links, same here var editLinks = document.querySelectorAll( '.grid-item.category-editorial a' ); for (var i = 0; i < editLinks.length; i++) { editLinks[i].setAttribute('target', '_blank'); editLinks[i].setAttribute('rel', 'noopener'); } } // Add current year to copyright notices document.getElementById( 'js-current-year' ).textContent = new Date().getFullYear(); // Call functions after page load extLink(); //addAlly(); setTimeout(function() { //addAlly(); //ideally we should only need to run it once ↑ }, 5000); }; function closeCookieDialog (){ document.getElementById("cookie-consent").style.display = "none"; return false; } setTimeout ( function () { closeCookieDialog(); }, 15000); console.log(`%c 🚀🚀🚀 ███ █████ ███████ █████████ ███████████ █████████████ ███████████████ ███████ ███████ ███████ ┌───────────────────────────────────────────────────────────────────┐ │ │ │ Towards AI is looking for contributors! │ │ Join us in creating awesome AI content. │ │ Let's build the future of AI together → │ │ https://towardsai.net/contribute │ │ │ └───────────────────────────────────────────────────────────────────┘ `, `background: ; color: #00adff; font-size: large`); //Remove latest category across site document.querySelectorAll('a[rel="category tag"]').forEach(function(el) { if (el.textContent.trim() === 'Latest') { // Remove the two consecutive spaces (  ) if (el.nextSibling && el.nextSibling.nodeValue.includes('\u00A0\u00A0')) { el.nextSibling.nodeValue = ''; // Remove the spaces } el.style.display = 'none'; // Hide the element } }); // Add cross-domain measurement, anonymize IPs 'use strict'; //var ga = gtag; ga('config', 'G-9D3HKKFV1Q', 'auto', { /*'allowLinker': true,*/ 'anonymize_ip': true/*, 'linker': { 'domains': [ 'medium.com/towards-artificial-intelligence', 'datasets.towardsai.net', 'rss.towardsai.net', 'feed.towardsai.net', 'contribute.towardsai.net', 'members.towardsai.net', 'pub.towardsai.net', 'news.towardsai.net' ] } */ }); ga('send', 'pageview'); -->