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Best Laptops for Machine Learning, Data Science, and Deep Learning
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Best Laptops for Deep Learning, Machine Learning (ML), and Data Science for 2023

Last Updated on March 5, 2023 by Editorial Team


Source: Image generated with generative AI via Midjourney. 

Get ahead in the AI game with our top picks for laptops that are perfect for machine learning, data science, and deep learning at every budget. After analyzing over 8,000 options [8], we’ve identified the best of the best to help future-proof your AI rig.

Last updated March 5, 2023

Are you tired of endlessly scouring the internet for the perfect laptop to power your machine learning, deep learning, and data science projects? Well, search no further! We’ve done the heavy lifting for you by sifting through a whopping 8,000 laptops to bring you only the most powerful and efficient machines for every budget. Whether you’re looking for top-of-the-line laptops or more affordable options, we’ve got you covered. With our expert recommendations, you can stay ahead of the game and future-proof your AI setup.

We’ll keep updating this resource as technology evolves to provide you with even more powerful and efficient laptops for every budget. Our inbox is inundated with emails from fellow AI enthusiasts who are seeking the best laptops for their AI projects, which inspired us to create this list. If you have any suggestions to add to the list, please feel free to shoot us an email at pub@towardsai.net.

Disclosure: Our editorial team at Towards AI writes authentic and trustworthy reviews and may receive a small compensation for products we select to support Towards AI’s efforts. For this article, as an Amazon Associate, Towards AI may receive a small commission from qualifying purchases made from it (at no extra cost to the buyer). For feedback, questions, or concerns, please email us at pub@towardsai.net.

Key Considerations for Choosing a Laptop for ML and Data Science

  1. Processor: A powerful CPU is crucial for running complex ML algorithms and data analysis. Look for a laptop with an Intel Core i7 or i9 processor or an AMD Ryzen 7 or 9 processor.
  2. GPU: The GPU (graphics processing unit) is important for running deep learning algorithms, as it can handle parallel processing much faster than a CPU. Look for a laptop with a dedicated GPU, such as an NVIDIA GeForce or Quadro, or an AMD Radeon.
  3. RAM: The more RAM your laptop has, the better it can handle large data sets and complex ML models. Look for a laptop with at least 16GB of RAM, but 32GB or more is ideal.
  4. Storage: Data sets for deep learning and data science can be massive, so you’ll want a laptop with plenty of storage. Look for a laptop with at least a 512GB SSD, or consider one with multiple drives or the ability to add external storage.
  5. Display: A high-quality display is important for visualizing data and models. Look for a laptop with a resolution of at least 1080p, or consider a 4K display for even more detail. You may also want to consider a laptop with a high color gamut, such as Adobe RGB or DCI-P3.

By considering these factors, you can find a laptop that will handle the demands of deep learning, machine learning and data science, allowing you to work efficiently and effectively.

???? Check out our editorial recommendations for the best deep learning workstations. ????

Let’s get started!

For Budgets under $ 1,000.00 ↓

Source: Amazon

Lenovo Legion 5

Updated: [Tie] Best laptop under $ 1k. Ideal for data leaders who care about AMD processors, excellent RAM size, and an RTX 3050ti GPU under a $ 1k budget.

Specs:

  • Processor: AMD Ryzen 5 5600H (Hexa-core, 12 Threads, base clock speed 3.3 GHz, max turbo to 4.2GHz, 16MB L3 Cache)
  • Memory: 32GB (16GB x 2) DDR4 3200MHz
  • Hard Drive: 2TB PCIe SSD
  • GPU: Dedicated NVIDIA GeForce RTX 3050 Ti 4 GB GDDR6, Boost clock up to 1695MHz, TGP up to 95W
  • Computing Power: 8.6 [9]
  • Ports: 1x USB-C 3.2 Gen 2 (support data transfer, Power Delivery, and DisplayPort 1.4), 1x USB-C 3.2 Gen 2 (support data transfer and DisplayPort 1.4), 1x USB 3.2 Gen 1 (Always On), 3x USB 3.2 Gen 1
    1x HDMI 2.1, 1x Ethernet (RJ-45), 1x Headphone/microphone combo jack (3.5mm)
  • OS: Windows 11 Home
  • Weight: 5.3 lbs
  • Display: 15.6″ Full HD (1920×1080) IPS 250nits Anti-glare, 45% NTSC, 120Hz, FreeSync
  • Connectivity: Wi-Fi 6, 11ax 2×2 + Bluetooth 5.1
  • Battery life: Average ~ 4 hours.

Grab one on Amazon


Source: Amazon

ASUS TUF Gaming A15

Updated: [Tie] Best laptop under $ 1k. Ideal for data leaders who care about Intel processors, suitable RAM size, and RTX 3050ti GPUs under a $ 1k budget.

Specs:

  • Processor: AMD Ryzen 7 8-core Processor AMD R7–6800H 16 MB Cache, Base Clock 3.2Ghz, Max Boost Clock 4.7Ghz,
  • Memory: 32GB DDR5 Memory
  • Hard Drives: 1TB SSD
  • GPU: NVIDIA GeForce RTX 3050 Ti 4 GB.
  • Computing Power: 8.6 [9]
  • Ports: 2 X USB 3.1 Type A | 1 X DisplayPort | 1 X RJ-45 | 1 X Headphone/Speaker/Line-Out Jack | 2 X USB 3.1 TYPE-C | 1 X HDMI |
  • OS: Windows 10 Home.
  • Weight: 4.85 lbs.
  • Display: 144Hz Full HD 1920×1080 display
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Battery life: Average ~ 4 hours.

Grab one on Amazon


Source: Amazon

Dell Mytrix G15

Updated: Fantastic laptop under $ 1k. Ideal for those who care about AMD processors, suitable RAM size, and RTX 30XX GPUs under a $ 1k budget.

Specs:

  • Processor: AMD Ryzen 5 5600H 3.30 GHz
  • Memory: 16 GB DDR4.
  • Hard Drives: 512 GB NVMe SSD.
  • GPU: NVIDIA GeForce RTX 3050 Ti 4 GB.
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 10 Home.
  • Weight: 5.39 lbs.
  • Display: 15.6, 1920 x 1080.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Battery life: Average ~ 4 hours.

Grab one on Amazon


Performance comparison RTX 3060 vs. RTX 2070 [11]

For Budgets under $ 2,000.00 ↓

Source: Amazon

Acer Predator Helios 300

Updated: [Tie] Best laptop under $ 2k. Ideal for data leaders who want the best under a $ 2k budget, care about Intel processors, excellent RAM size, and RTX 30XX GPUs.

Specs:

  • Processor: Intel Core i7 -11800H up to 4.6GHz
  • Memory: 64 GB DDR4.
  • Hard Drives: 1 TB NVMe SSD.
  • GPU: NVIDIA GeForce RTX 3060 4 GB.
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 10 Home.
  • Weight: 5.07 lbs.
  • Display: 15.6, 1920 x 1080.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Battery life: Average ~ 6 hours.

Grab one on Amazon


Source: Amazon

CUK Katana 15

Best laptop under $ 2k. Ideal for data leaders who want the best under a $ 2k budget, care about Intel processors, excellent RAM size, and RTX 30XX GPUs.

Specs:

  • Processor: Intel Core i7 -12650H up to 4.8GHz
  • Memory: 64 GB DDR5
  • Hard Drives: 2 TB NVMe SSD
  • GPU: NVIDIA GeForce RTX 4070 8 GB GDDR6
  • Computing Power: 8.9 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0
  • OS: Windows 11
  • Weight: 4.96 lbs
  • Display: 15.6, 1920 x 1080 144Hz
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth
  • Battery life: Average ~ 6 hours

Grab one on Amazon


Source: Amazon

Eluktronics MECH 17

One of the best laptops under $ 2k. Ideal for data leaders who want the best under a $ 2k budget, care about AMD processors, excellent RAM size, and RTX 30XX GPUs.

Specs:

  • Processor: AMD Ryzen 9 6900HX
  • Memory: 32 GB DDR5
  • Hard Drives: 2 TB NVMe SSD
  • GPU: NVIDIA GeForce RTX 3080 Ti
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.1, 1x USB 3.2 Type-C, 1x USB 3.1, SD Card Reader, 2,500GB Ethernet RJ-45, Audio out & Mic In, Kensington Lock slot
  • OS: Windows 11
  • Weight: 5.95 lbs
  • Display: 17, 2560 x 1600
  • Connectivity: Intel WiFi 6 & Bluetooth 5.2
  • Battery life: Average ~ 6 hours

Grab one on Amazon


Source: Amazon

ASUS ROG Strix Scar 15

Fantastic and affordable laptop under $ 2k. Ideal for data leaders who want the best under a $ 2k budget, care about Intel processors, suitable RAM size, and RTX 30XX GPUs.

Specs:

  • Processor: AMD Ryzen 9 5900 HX
  • Memory: 32GB RAM
  • Hard Drives: 1TB SSD
  • GPU: NVIDIA GeForce RTX 3080 8GB GDDR6
  • Computing Power: 8.6 [9]
  • Ports: 1 Thunderbolt 3 Port, 1 HDMI x 2.0, 3 USB 3.2 (Gen 1), 1 USB 3.2 Type C
  • OS: Windows 11
  • Weight: 5.07
  • Display: 15.6 1920 x 1080
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth 5.1.
  • Battery life: ~3–4 hours.

Grab one on Amazon


RTX 4070 Ti vs. RTX 3080 Ti [14]

For Budgets under $ 3,000.00 ↓

Source: Amazon

CUK AORUS 17H

Best rig for under $ 3k hands down. The updated CUK AORUS 17H laptop is a beast and makes a powerful laptop. The prefered AI rig for data leaders with a preference for Intel CPUs and a massive NVIDIA 40XX GPU.

Specs:

  • Processor: Intel Core i7–13700H
  • Memory: 32GB RAM DDR5.
  • Hard Drives: 2TB NVMe SSD.
  • GPU: NVIDIA RTX 4080 12GB GDDR
  • XComputing Power: 8.9 [9]
  • Ports: 1x HDMI, 1x Thunderbolt 3, 1USB 3.1 Gen 2, 2x USB 3.2.
  • OS: Windows 11
  • Weight: 5.95 lbs.
  • Display: 17.3, 2560–1440 FHD 360Hz.
  • Connectivity: WiFi 802.11ac, Gigabit LAN (Ethernet), Bluetooth.
  • Battery life: Better~ 4-5 hours.

Grab one on Amazon


Source: Amazon

MSI Raider GE77Hx

One of the best rigs for under $ 3k. This MSI Raider laptop is a beast as well. This AI rig is the prefered choice for data leaders with an inclination for Intel CPUs.

Specs:

  • Processor: Intel Core i9–12900HX 5 GHz
  • Memory: 32GB RAM DDR5
  • Hard Drives: 2TB PCle Gen 4 SSD.
  • GPU: NVIDIA RTX 3080 Ti
  • Computing Power: 8.6 [9]
  • Ports: Thunderbolt 4, USB-Type C
  • OS: Windows 11 Pro
  • Weight: 9.9lbs.
  • Display: 17.3, 2560–1440 QHD 240 Hz
  • Connectivity: WiFi 802.11ac, Gigabit LAN (Ethernet), Bluetooth.
  • Battery life: Better~ 5 hours.

Grab one on Amazon


Source: Amazon

Acer Predator Triton 500 SE

Updated: Best laptop for those looking for top performance on all ends for under ~$2.5k.

Specs:

  • Processor: Intel i9–12900H 5 GHz
  • Memory: 32GB RAM LPDDR5
  • Hard Drives: 1TB NVMe SSD
  • GPU: NVIDIA RTX 3080 Ti 16GB
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI, 1x Thunderbolt 3, 1x USB-C, 2x USB 3.2
  • OS: Windows 10 Pro
  • Weight: 5.29
  • Display: 16 WQXGA 240 Hz
  • Connectivity: Killer Wi-Fi 6E
  • Battery life: Not benchmarked yet

Grab one on Amazon


Monster Rigs (Unlimited Budget)

Source: Amazon

DELL Precision 7770 AI

Insane rig, best for data leaders inclined for Intel CPUs looking for top performance on all ends, and for a very well-known brand, such as DELL with a massive 128GB of RAM.

Specs:

  • Processor: Intel 12th Gen i9–12950HX
  • Memory: 128GB RAM DDR5.
  • Hard Drives: 8TB NVMe SSD.
  • GPU: NVIDIA RTX A5500 16GB GDDR6[1].
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI, 1x Thunderbolt 3, 1x USB-C, 2x USB 3.2.
  • OS: Windows 11 Pro
  • Weight: 6.73 lbs
  • Display: 17.3 UHD
  • Connectivity: Intel Wifi 6E (6GHz) AX211 2×2 with Bluetooth Wireless Primary
  • Battery life: Not benchmarked yet.

Grab one on Amazon


Source: Amazon

MSI WE76 11UM

Best monster rig for data leaders inclined for AMD CPUs looking for top performance on all ends. This Prometheus laptop is a beast as well, and the top of the line of our Eluktronics recommendation based on specs.

Specs:

  • Processor: Intel Core i9–11980HK
  • Memory: 64GB RAM DDR4
  • Hard Drives: 1TB NVME SSD
  • GPU: NVIDIA RTX A5000 16GB [1]
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI, 1x Thunderbolt 3, 1USB 3.1 Gen 2, 2x USB 3.2
  • OS: Windows 10 Pro
  • Weight: 6.39lbs
  • Display: 17.3, 1920×1080
  • Connectivity: WiFi 802.11ac, Gigabit LAN (Ethernet), Bluetooth
  • Battery life: Better~ 5 hours

Grab one on Amazon


Final Thoughts

Finding the right laptop for deep learning, machine learning, and data science is crucial for those looking to work efficiently and effectively in these fields. As technology evolves, it is essential to stay up-to-date with the latest advancements and innovations. After analyzing over 8,000 laptops, we have identified the best laptops for every budget, from the Lenovo Legion 5 for budgets under $1,000 to the CUK AORUS 17H for budgets under $3,000.

When choosing a laptop, it is important to consider factors such as the processor, GPU, RAM, storage, and display. A powerful CPU is crucial for running complex ML algorithms and data analysis. The GPU is important for running deep learning algorithms, and the more RAM your laptop has, the better it can handle large data sets and complex ML models. Additionally, data sets for deep learning and data science can be massive, so you’ll want a laptop with plenty of storage. Finally, a high-quality display is important for visualizing data and models.

Overall, with the right laptop, you can future-proof your AI setup and stay ahead of the game in the world of deep learning, machine learning, and data science. If you come across any phenomenal laptops, such as those mentioned in this list, please let us know by emailing us.

Thank you for reading!

References

[1] Max-Q Design, Nvidia, https://www.nvidia.com/en-us/geforce/gaming-laptops/max-q/

[2] Intel 10750H Q2 2020, Intel, https://www.intel.com/content/www/us/en/products/processors/core/i7-processors/i7-10750h.html

[3] Intel 9750H, Intel, https://www.intel.com/content/www/us/en/products/processors/core/i7-processors/i7-9750h.html

[4] AMD Ryzen 7 7800H, AMD, https://www.amd.com/en/products/apu/amd-ryzen-7-4800h

[5] Intel 10980 HK, Intel, https://ark.intel.com/content/www/us/en/ark/products/201838/intel-core-i9-10980hk-processor-16m-cache-up-to-5-30-ghz.html

[6] Intel 10875 HK. Intel, https://ark.intel.com/content/www/us/en/ark/products/202329/intel-core-i7-10875h-processor-16m-cache-up-to-5-10-ghz.html

[7] RTX 2080 vs. AMD Radeon Pro 5500M, User Benchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-2080-vs-AMD-Radeon-Pro-5500M/4026vsm960765

[8] RTX Performance Laptops, Amazon, https://www.amazon.com/s?k=rtx+laptop&rh=n%3A565108&ref=nb_sb_noss

[9] NVidia CUDA Geforce GPUS, Nvidia, https://developer.nvidia.com/cuda-gpus

[10] Nvidia CUDA Quadro GPUS, Nvidia, https://www.nvidia.com/object/quadro-for-mobile-workstations.html

[11] GPU UserBenchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-3060-vs-Nvidia-RTX-2070S-Super-Mobile-Max-Q/4105vsm1168355

[12] GPU UserBenchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-3080-Laptop-vs-Nvidia-RTX-2080S-Super-Mobile-Max-Q/m1443565vsm1114823

[13] CUDA-Enabled GeForce and TITAN Products, https://developer.nvidia.com/cuda-gpus

[14] GPU UserBenchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4070-Ti-vs-Nvidia-RTX-3080-Ti/4146vs4115

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//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 = [ /* ' ' + '

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',*/ ]; 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'); -->