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

APTOS 2019 Blindness Detection — Playing around with ResNeXts and Progressive NASNet
Latest   Machine Learning   Newsletter

APTOS 2019 Blindness Detection — Playing around with ResNeXts and Progressive NASNet

Last Updated on July 24, 2023 by Editorial Team

Author(s): Luka Chkhetiani

Originally published on Towards AI.

APTOS 2019 Blindness Detection U+007C Towards AI

A while ago Kaggle announced challenge: APTOS 2019 Blindness Detection — Detect diabetic retinopathy to stop blindness before it’s too late.

The idea of competition is to predict the severity of diabetic retinopathy on a scale of 0–4.

0 - No DR1 - Mild2 - Moderate3 - Severe4 - Proliferative DR

The training set consists of 3661 images and 4 classes. We should note that the dataset is not well-balanced. Well, we’ll use a couple of tricks to maximize the possible outcome, including augmentation, freezing & unfreezing layers, etc.

I’ve tried a couple of models, such as DenseNet, ResNet50/101, Inception v3/v4 and even ResNeXt-101–32x8d — architecture with 88M parameters, 82.2 top-1 and 96.4 top-5 errors weren’t able to surpass the 66.2% accuracy limit on submission set, nonetheless, some of them have shown >95.0% acc on validation.

So, I sat down and analyzed the dataset, and a couple of models.
Idea is that — big models overfitted, and small models under fitted, no matter how good I’ve tuned them.

Searched through models once again, and I got:

And, decided to try both models.

I’ve implemented TensorboardX , which generates ‘run’ directory after execution, so we can visualize the training process.

torch.DataParallel?
No, I’m a victim of Google Colab for now.

My PyTorch code for ResNeXt50 training:

For PNASNet 5 Large:

I used Cadene’s implementation initially, and then converted to my code for fine-tuning, by just loading the model.

Training Set

After combining the datasets, the total number of images per class looks like the next:

Total - 3661 images


0 - No DR : 1805 images
1 - Mild : 370 images2 - Moderate : 999 images3 - Severe : 193 images4 - Proliferative DR : 295 images

Actually, there’s a huge difference between the classes. We’ll use augmentation, but anyway — augmenting images can help, but not a lot.

A simple code that will help you to sort the images by classes after unzipping them would be:

Data Augmentation

Dataset is not so rich. We’ve 3661 images total. So, I’m going to use PyTorch’s data augmentation techniques, such as:

transforms.RandomVerticalFlip(p=0.5),

transforms.RandomRotation((0,360), center=None),

transforms.RandomHorizontalFlip(p=0.5)

Randomly flipping images vertically with 0.5 probability.
Random Rotation within 0,359 range (basically giving the ability to make a full rotation)
Randomly flipping horizontally with 0.5 probability.

PyTorch data transformation techniques work perfectly. By the end of the epoch, the model will have seen additionally 3 different augmentations on a single image.

Data Resizing

ResNeXt is being trained on 299×299 resized images. But, PNASNet requires 331×331 inputs. Thus, I’m going to modify the code respectively.

Little UX:

In case we want the process to run in the background, and not have a laptop/PC up all night, we can use nohup.

The command will be:

nohup python3 train.py &

And, we can see the stdouts via:

cat nohup.out

Or, tail them via:

tail -f nohup.out

Additionally, I really didn’t want to mess with ngrok. So, I used subprocess function to download the tensorboard output every once in a while, and refresh it.

from time import sleepimport subprocessfor i in range(10000):subprocess.run('scp user@ip_address:~/APTOS/runs/Aug30_21-58-43/* runs/', shell=True)sleep(20)

Let’s start training.

Plan:

  • Train the ResNeXt for 3 and PNASNet for 2 epochs with learning rate 1e-3.
  • Train the ResNeXt and PNASNet for 3 epochs with learning rate 1e-4
  • ResNeXt — Freeze all layers but 3,4, decrease lr to 1e-5, and unfreeze the blocks step-by-step by decreasing lr by 10x.
  • PNASNet — Freeze everything but cell_9, 10, 11 and continue training on 1e-5 lr. Afterward, unfreeze the cells step by step and anneal the lr by 10x.

Why?

While playing around with those two models, I noticed that it yielded persuasive accuracy on the first two epochs with lr 1e-3, but if the training was continued with the same parameters, it drafted around the same accuracy and loss.
Decreasing lr 10x times helps a lot to increase accuracy and continue decreasing the loss. 4 epochs are perfectly enough for the whole model to get to know with the dataset and study enough features.

But, after leaving only the deepest layers, which are responsible for deeper features makes the model more sophisticated and fastens up the training procedure. To say it in other words: I’m giving the model some time to familiarize with the dataset and study it, and afterward concentrating it on the most powerful components of the dataset.

1st Part

Learning rate 1e-3, all layers, 2 epochs.

PNASNet 5 Large

Epoch 1/50.. Train loss: 3.503.. Test loss: 5.913.. Test accuracy: 0.011 
Epoch 1/50.. Train loss: 1.449.. Test loss: 1.084.. Test accuracy: 0.697
Epoch 1/50.. Train loss: 0.806.. Test loss: 0.673.. Test accuracy: 0.745
Epoch 1/50.. Train loss: 0.630.. Test loss: 0.606.. Test accuracy: 0.776
Epoch 1/50.. Train loss: 0.681.. Test loss: 0.591.. Test accuracy: 0.773
Epoch 2/50.. Train loss: 0.610.. Test loss: 0.546.. Test accuracy: 0.796
Epoch 2/50.. Train loss: 0.711.. Test loss: 0.564.. Test accuracy: 0.792
Epoch 2/50.. Train loss: 0.474.. Test loss: 0.582.. Test accuracy: 0.790
Epoch 2/50.. Train loss: 0.484.. Test loss: 0.539.. Test accuracy: 0.807
Epoch 2/50.. Train loss: 0.555.. Test loss: 0.527.. Test accuracy: 0.811
Epoch 3/50.. Train loss: 0.559.. Test loss: 0.520.. Test accuracy: 0.814
Epoch 3/50.. Train loss: 0.455.. Test loss: 0.507.. Test accuracy: 0.821
Epoch 3/50.. Train loss: 0.572.. Test loss: 0.486.. Test accuracy: 0.822
Epoch 3/50.. Train loss: 0.408.. Test loss: 0.520.. Test accuracy: 0.831
Epoch 3/50.. Train loss: 0.546.. Test loss: 0.466.. Test accuracy: 0.829

ResNeXt 50 32x4d

Epoch 1/50.. Train loss: 2.787.. Test loss: 3.990.. Test accuracy: 0.254 
Epoch 1/50.. Train loss: 1.460.. Test loss: 0.893.. Test accuracy: 0.684
Epoch 1/50.. Train loss: 0.722.. Test loss: 0.654.. Test accuracy: 0.738
Epoch 2/50.. Train loss: 0.817.. Test loss: 0.640.. Test accuracy: 0.771
Epoch 2/50.. Train loss: 0.590.. Test loss: 0.533.. Test accuracy: 0.807
Epoch 2/50.. Train loss: 0.517.. Test loss: 0.496.. Test accuracy: 0.820
Epoch 3/50.. Train loss: 0.579.. Test loss: 0.559.. Test accuracy: 0.772
Epoch 3/50.. Train loss: 0.470.. Test loss: 0.493.. Test accuracy: 0.809
Epoch 3/50.. Train loss: 0.512.. Test loss: 0.477.. Test accuracy: 0.836

2nd Part

All Layers

PNASNet 5 Large

Epoch 1/50.. Train loss: 0.472.. Test loss: 0.402.. Test accuracy: 0.848 
Epoch 1/50.. Train loss: 0.452.. Test loss: 0.370.. Test accuracy: 0.857
Epoch 1/50.. Train loss: 0.474.. Test loss: 0.383.. Test accuracy: 0.840
Epoch 1/50.. Train loss: 0.402.. Test loss: 0.383.. Test accuracy: 0.855
Epoch 2/50.. Train loss: 0.552.. Test loss: 0.398.. Test accuracy: 0.848
Epoch 2/50.. Train loss: 0.453.. Test loss: 0.378.. Test accuracy: 0.867
Epoch 2/50.. Train loss: 0.431.. Test loss: 0.391.. Test accuracy: 0.846
Epoch 2/50.. Train loss: 0.307.. Test loss: 0.379.. Test accuracy: 0.857
Epoch 3/50.. Train loss: 0.453.. Test loss: 0.378.. Test accuracy: 0.855
Epoch 3/50.. Train loss: 0.372.. Test loss: 0.376.. Test accuracy: 0.851
Epoch 3/50.. Train loss: 0.429.. Test loss: 0.380.. Test accuracy: 0.862
Epoch 3/50.. Train loss: 0.408.. Test loss: 0.385.. Test accuracy: 0.855

ResNeXt 50

Epoch 1/50.. Train loss: 0.230.. Test loss: 0.423.. Test accuracy: 0.837 
Epoch 1/50.. Train loss: 0.454.. Test loss: 0.416.. Test accuracy: 0.846
Epoch 1/50.. Train loss: 0.462.. Test loss: 0.410.. Test accuracy: 0.841
Epoch 2/50.. Train loss: 0.458.. Test loss: 0.416.. Test accuracy: 0.837
Epoch 2/50.. Train loss: 0.437.. Test loss: 0.386.. Test accuracy: 0.854
Epoch 2/50.. Train loss: 0.407.. Test loss: 0.406.. Test accuracy: 0.846
Epoch 3/50.. Train loss: 0.425.. Test loss: 0.401.. Test accuracy: 0.845
Epoch 3/50.. Train loss: 0.424.. Test loss: 0.394.. Test accuracy: 0.836
Epoch 3/50.. Train loss: 0.552.. Test loss: 0.398.. Test accuracy: 0.846

After freezing & unfreezing layers and tuning the lr

PNASNet 5 Large

Epoch 1/50.. Train loss: 0.220.. Test loss: 0.344.. Test accuracy: 0.884 
Epoch 1/50.. Train loss: 0.477.. Test loss: 0.361.. Test accuracy: 0.873
Epoch 1/50.. Train loss: 0.428.. Test loss: 0.345.. Test accuracy: 0.881
Epoch 1/50.. Train loss: 0.410.. Test loss: 0.358.. Test accuracy: 0.872
Epoch 1/50.. Train loss: 0.403.. Test loss: 0.356.. Test accuracy: 0.874
Epoch 1/50.. Train loss: 0.414.. Test loss: 0.332.. Test accuracy: 0.884

ResNeXt 50

Epoch 1/50.. Train loss: 0.176.. Test loss: 0.354.. Test accuracy: 0.876 
Epoch 1/50.. Train loss: 0.332.. Test loss: 0.370.. Test accuracy: 0.865
Epoch 2/50.. Train loss: 0.401.. Test loss: 0.361.. Test accuracy: 0.865
Epoch 2/50.. Train loss: 0.376.. Test loss: 0.366.. Test accuracy: 0.866
Epoch 2/50.. Train loss: 0.342.. Test loss: 0.354.. Test accuracy: 0.870
Epoch 3/50.. Train loss: 0.399.. Test loss: 0.372.. Test accuracy: 0.870
Epoch 3/50.. Train loss: 0.330.. Test loss: 0.349.. Test accuracy: 0.875

Results

We’ve 87.5% accuracy on ResNeXt 50, and 88.4% on PNASNet 5.

Basically, I’ve tried SOTA, mediocre, and lastly — two out of top architectures for image classification.
And, we know — the models don’t work exactly the same way in real life as they show in test/validation procedures.

Anyway, tuning two models overnight was fun, and lastly — I’ll make predictions on actual test (submission) set, and we’ll see the results.

Prediction

My inference for the prediction part.

In case we’re using Google Colab for prediction, we should note that sometimes tqdm is not a great option, as long as it refreshes the stdout for every output, and the page will crash. I always try with tqdm firstly, and if it’s not working well, just erasing it on the loop, and writing my version of the script to see the prediction process. We should make sure that we erase the *.csv file after the first try, or it’ll append the new predictions to it.

Kaggle

https://www.kaggle.com

While trying to make a submission after the prediction part, kaggle made me furious. Basically they had a bug in the kernel, that threw submission error every time I tried to submit the predictions.
After searching for a while, I saw one kaggler’s comment, that actually helped me.

P.S. Turning off GPU and Internet in Kernel were helpful as well, in addition to downgrading Python docker image to 1–7 versions on Kaggle.

I’m gonna lend their code for submission part:

Lended from https://www.kaggle.com/kinnachen’s comment

Final Results:

ResNeXt 50

PNASNet 5

When I checked the reason why PNASNet worked so poorly, I noticed many of 0’s and 2’s, a couple of 1’s as prediction numbers in submission. And absolutely no 4’s or 3’s.
NASNet overfitted on 0 and 2 classes, as long as they held the most of the data, and made poor predictions on other classes, or didn’t make at all.

And, ResNeXt 50 turned out to work well for just overnight tuning.

There are 3 more days until the challenge closes, and I’m planning to try more
promising approaches as soon as I have time.

Hope you enjoyed it!

UPDATE:

By cropping dataset, and training the last layers longer, I made 4.1% improvement.

2nd UPDATE

Mean color subtraction gave 2.0% improvement.

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

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'); -->