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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!

Author: Shenggang Li

Adaptive Decay-Weighted ARMA: A Novel Approach to Time Series Forecasting
Latest  Machine Learning

Adaptive Decay-Weighted ARMA: A Novel Approach to Time Series Forecasting

Shenggang Li
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April 12, 2025

Author(s): Shenggang Li Originally published on Towards AI. Integrating Recency-Based Loss Weighting and Seasonal Feature Tuning for Enhanced Predictive AccuracyPhoto by Haberdoedas II on Unsplash Time series forecasting is both fascinating and challenging. It’s fascinating because accurate predictions can directly inform better …

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Beyond Simple Inversion: Building and Applying Inverse Neural Networks
Data Science  Latest  Machine Learning

Beyond Simple Inversion: Building and Applying Inverse Neural Networks

Shenggang Li
1 like
April 7, 2025

Author(s): Shenggang Li Originally published on Towards AI. Theory, training tricks, and real‑world case studies — solving multi‑root equations and beyondPhoto by Marisa Harris on Unsplash Inverse problems ask a fundamental question: Given the output y, what was the input x? Traditional …

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Reinforcement Learning-Enhanced Gradient Boosting Machines
Latest  Machine Learning

Reinforcement Learning-Enhanced Gradient Boosting Machines

Shenggang Li
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April 1, 2025

Author(s): Shenggang Li Originally published on Towards AI. A Novel Approach to Integrating Reinforcement Learning within Gradient Boosting Internal Optimization for Superior Predictive PerformancePhoto by Austin Neill on Unsplash In this post, I demonstrate how reinforcement learning (RL) can directly enhance the …

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Adaptive Multi-Teacher Distillation for Enhanced Supervised Learning
Latest  Machine Learning

Adaptive Multi-Teacher Distillation for Enhanced Supervised Learning

Shenggang Li
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March 24, 2025

Author(s): Shenggang Li Originally published on Towards AI. A Novel Approach for Dynamically Combining Multiple Predictive Models into a Lightweight High-Performance Student ModelPhoto by Damon Hall on Unsplash In practical supervised learning, using a single predictive model like XGBoost, LightGBM, or Random …

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Reimagining Diffusion Models: Autoregressive Priors for Efficient Initialization
Latest  Machine Learning

Reimagining Diffusion Models: Autoregressive Priors for Efficient Initialization

Shenggang Li
1 like
March 8, 2025

Author(s): Shenggang Li Originally published on Towards AI. Exploring a Novel Approach to Diffusion Initialization with Intuitive Illustrations, Applications This member-only story is on us. Upgrade to access all of Medium. Photo by Gary Fultz on Unsplash Diffusion models have become a …

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Practical Guide to Distilling Large Models into Small Models: A Novel Approach with Extended Distillation
Latest  Machine Learning

Practical Guide to Distilling Large Models into Small Models: A Novel Approach with Extended Distillation

Shenggang Li
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March 3, 2025

Author(s): Shenggang Li Originally published on Towards AI. Comparing Traditional and Enhanced Step-by-Step Distillation: Adaptive Learning, Cosine Similarity, and Curriculum-Based Rationale Supervision This member-only story is on us. Upgrade to access all of Medium. Photo by Thorium on Unsplash In this paper, …

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Exploring LoRA as a Dynamic Neural Network Layer for Efficient LLM Adaptation
Latest  Machine Learning

Exploring LoRA as a Dynamic Neural Network Layer for Efficient LLM Adaptation

Shenggang Li
0 like
February 26, 2025

Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Photo by Jakub Żerdzicki on Unsplash LLMs need constant updates — legal AI must learn new laws, finance chatbots need fresh market …

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Building AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI
Latest  Machine Learning

Building AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI

Shenggang Li
0 like
February 20, 2025

Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. A Step-by-Step Guide to Configuring Google Vertex AI, Leveraging the Gemini API, and Integrating Knowledge Bases for Intelligent Conversational Applications Photo by …

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Investigating Transformer Attention and Reinforcement Learning Dynamics Using Self‑Generated Structural Data
Latest  Machine Learning

Investigating Transformer Attention and Reinforcement Learning Dynamics Using Self‑Generated Structural Data

Shenggang Li
1 like
February 14, 2025

Author(s): Shenggang Li Originally published on Towards AI. Cracking the Code: Synthetic Data as the Key to Understanding and Enhancing LLMs This member-only story is on us. Upgrade to access all of Medium. Photo by Joshua Sortino on Unsplash Building large language …

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A Neural Sparse Graphical Model for Variable Selection and Time-Series Network Analysis
Latest  Machine Learning

A Neural Sparse Graphical Model for Variable Selection and Time-Series Network Analysis

Shenggang Li
0 like
February 10, 2025

Author(s): Shenggang Li Originally published on Towards AI. A Unified Adjacency Learning and Nonlinear Forecasting Framework for High-Dimensional Data This member-only story is on us. Upgrade to access all of Medium. Photo by Susan Q Yin on Unsplash Imagine a spreadsheet with …

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Beyond Buy-and-Hold: Dynamic Strategies for Unlocking Long-Term Stock Growth
Latest  Machine Learning

Beyond Buy-and-Hold: Dynamic Strategies for Unlocking Long-Term Stock Growth

Shenggang Li
1 like
February 7, 2025

Author(s): Shenggang Li Originally published on Towards AI. Harnessing Survival Analysis and Markov Decision Processes to Surpass Static ETF Performance This member-only story is on us. Upgrade to access all of Medium. Photo by Chris Liverani on Unsplash Have you ever heard …

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DeepSeek-TS+: A Unified Framework for Multi-Product Time Series Forecasting
Latest  Machine Learning

DeepSeek-TS+: A Unified Framework for Multi-Product Time Series Forecasting

Shenggang Li
0 like
February 4, 2025

Author(s): Shenggang Li Originally published on Towards AI. Leveraging State-Space Enhanced Multi-Head Latent Attention and Group Relative Policy Optimization (GRPO) for Adaptive Forecasting This member-only story is on us. Upgrade to access all of Medium. Photo by Solen Feyissa on Unsplash I …

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Reinforcement Learning-Driven Adaptive Model Selection and Blending for Supervised Learning
Latest  Machine Learning

Reinforcement Learning-Driven Adaptive Model Selection and Blending for Supervised Learning

Shenggang Li
1 like
February 3, 2025

Author(s): Shenggang Li Originally published on Towards AI. Inspired by Deepseeker: Dynamically Choosing and Combining ML Models for Optimal Performance This member-only story is on us. Upgrade to access all of Medium. Photo by Agence Olloweb on Unsplash Machine learning model selection …

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Event-Driven Prediction: Expanding Mamba State Space Models for Conditional Forecasting
Latest  Machine Learning

Event-Driven Prediction: Expanding Mamba State Space Models for Conditional Forecasting

Shenggang Li
0 like
January 30, 2025

Author(s): Shenggang Li Originally published on Towards AI. A Novel Approach Combining Markov Decision Theory and Neural State Space Models for Stock Price Prediction This member-only story is on us. Upgrade to access all of Medium. Photo by Ella Jardim on Unsplash …

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Unlocking the Potential of Meta LLaMA: A Deep Dive into Its Design, Architecture, and Applications
Latest  Machine Learning

Unlocking the Potential of Meta LLaMA: A Deep Dive into Its Design, Architecture, and Applications

Shenggang Li
0 like
January 28, 2025

Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Photo by Dima Solomin on Unsplash Let’s explore Meta’s LLaMA, one of the coolest and most powerful language models. If you’ve ever …

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`; } 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 = [ /* ' ' + '

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',*/ ]; var replaceText = { '': '', '': '', '
': '
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', }; 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'); -->