![Revisit consent button](../images/body-df29beacd7496220cc94.svg) We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg) We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more NecessaryAlways Active Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data. * Cookie BCTempID * Duration 10 minutes * Description No description available. * Cookie __cf_bm * Duration 1 hour * Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. * Cookie AWSALBCORS * Duration 7 days * Description Amazon Web Services set this cookie for load balancing. * Cookie _cfuvid * Duration session * Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services * Cookie li_gc * Duration 6 months * Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes. * Cookie __hssrc * Duration session * Description This cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session. * Cookie __hssc * Duration 1 hour * Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie. * Cookie wpEmojiSettingsSupports * Duration session * Description WordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly. * Cookie BCSessionID * Duration 1 year 1 month 4 days * Description Blueconic sets this cookie as a unique identifier for the BlueConic profile. * Cookie _octo * Duration 1 year * Description No description available. * Cookie logged_in * Duration 1 year * Description No description available. * Cookie __Secure-YEC * Duration past * Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video * Cookie __eoi * Duration 6 months * Description Description is currently not available. * Cookie AWSALBTGCORS * Duration 7 days * Description No description available. * Cookie login-status-p * Duration past * Description Description is currently not available. * Cookie AWSALBTG * Duration 7 days * Description No description available. * Cookie csrf_token * Duration session * Description No description available. * Cookie token_v2 * Duration 1 day * Description Description is currently not available. * Cookie D * Duration 1 year * Description Description is currently not available. * Cookie PHPSESSID * Duration session * Description This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. * Cookie VISITOR_PRIVACY_METADATA * Duration 6 months * Description YouTube sets this cookie to store the user's cookie consent state for the current domain. * Cookie cookietest * Duration session * Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience. * Cookie __Host-airtable-session * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie __Host-airtable-session.sig * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie m * Duration 1 year 1 month 4 days * Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly. * Cookie BIGipServer* * Duration session * Description Marketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. * Cookie __cfruid * Duration session * Description Cloudflare sets this cookie to identify trusted web traffic. * Cookie _GRECAPTCHA * Duration 6 months * Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks. * Cookie __Secure-YNID * Duration 6 months * Description Google cookie used to protect user security and prevent fraud, especially during the login process. * Cookie cookieyes-consent * Duration 1 year * Description CookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors. Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features. * Cookie lidc * Duration 1 day * Description LinkedIn sets the lidc cookie to facilitate data center selection. * Cookie brw * Duration 1 year * Description No description available. * Cookie brwConsent * Duration 5 minutes * Description Description is currently not available. * Cookie WMF-Uniq * Duration 1 year * Description Description is currently not available. * Cookie loom_anon_comment * Duration 1 year * Description No description available. * Cookie loom_referral_video * Duration session * Description Description is currently not available. * Cookie VISITOR_INFO1_LIVE * Duration 6 months * Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. * Cookie yt-remote-connected-devices * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie ytidb::LAST_RESULT_ENTRY_KEY * Duration Never Expires * Description The cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future. * Cookie yt-remote-device-id * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie yt-remote-session-name * Duration session * Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video. * Cookie yt-remote-fast-check-period * Duration session * Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos. * Cookie yt-remote-session-app * Duration session * Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player. * Cookie yt-remote-cast-available * Duration session * Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player. * Cookie yt-remote-cast-installed * Duration session * Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video. * Cookie cp_session * Duration 3 months * Description Codepen sets this cookie for Help systems found in the website. * Cookie loid * Duration 1 year 1 month 4 days * Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform. Analytics Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc. * Cookie __hstc * Duration 6 months * Description Hubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session). * Cookie hubspotutk * Duration 6 months * Description HubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts. * Cookie _ga * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. * Cookie _ga_* * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to store and count page views. * Cookie __Host-psifi.analyticsTrace * Duration 6 hours * Description Description is currently not available. * Cookie __Host-psifi.analyticsTraceV2 * Duration 6 hours * Description Description is currently not available. * Cookie _gh_sess * Duration session * Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form. * Cookie YSC * Duration session * Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. * Cookie ajs_anonymous_id * Duration 1 year * Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before. * Cookie vuid * Duration 1 year 1 month 4 days * Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. Performance Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors. * Cookie AWSALB * Duration 7 days * Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target. * Cookie acq * Duration past * Description Description is currently not available. * Cookie acq.sig * Duration past * Description Description is currently not available. * Cookie ptc * Duration 2 years * Description No description available. Advertisement Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns. * Cookie muc_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website. * Cookie guest_id_marketing * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie guest_id_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie personalization_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting. * Cookie guest_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences. * Cookie bcookie * Duration 1 year * Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs. * Cookie __Secure-ROLLOUT_TOKEN * Duration 6 months * Description YouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment. * Cookie yt.innertube::nextId * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie yt.innertube::requests * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie session_tracker * Duration session * Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website. * Cookie edgebucket * Duration session * Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content. * Cookie did * Duration 1 year * Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year. Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation [](https://towardsdatascience.com/) Publish AI, ML & data-science insights to a global community of data professionals. Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) # Beam Search: the Most Used Algorithm in Sequence Models Learn the working principles of the most famous algorithm for text translation and speech recognition. [Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/) December 13, 20236 min read ![Beam Search allows to consider multiple streams of candidates. Image source: unsplash.com.](../images/000.jpeg) Beam Search allows to consider multiple streams of candidates. Image source: [unsplash.com](https://unsplash.com/photos/multi-colored-light-streaks-on-white-background-ylMP3TetKoQ). Imagine you're an [AI](https://en.wikipedia.org/wiki/Artificial_intelligence) language model, like [ChatGPT](https://chat.openai.com/), completing a sentence. How do you choose the next word to make it not just **grammatically correct** but also **contextually relevant**? This is where [**Beam Search**](https://en.wikipedia.org/wiki/Beam_search#:~:text=In%20computer%20science%2C%20beam%20search,that%20reduces%20its%20memory%20requirements.) steps in. By efficiently **exploring multiple possibilities** in parallel and maintaining top candidates at each step, Beam Search plays a crucial role in the task of predicting **subsequent elements**. Being an effective and powerful algorithm, it ensures output aligns with grammatical constraints and the context. To understand Beam Search's impact, think about all the applications requiring precise sequence generation, like **language translation** , **text completion** , and **chatbots**. In all these applications, Beam Search plays a critical role. In this article, I will introduce the theory and guide you through a practical step-by-step example of the Beam Search algorithm. I will also present several Beam Search variants, and detail all the pros and cons of this fundamental algorithm. Learn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps. ## Understanding Beam Search Imagine you need to **translate the following sentence** from Spanish to English: > Pablo estará en Nueva York la próxima semana. We don't only want to obtain a correct translation, we want to obtain **the best one**. For a language model, the best output coincides with the **most probable one**. To achieve this task, most [sequence-to-sequence](https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html) models employ Beam Search. It serves as an heuristic algorithm, systematically exploring multiple possibilities in parallel. At each step, a defined "beam width" maintains a fixed number of top candidates. This allows the algorithm to explore several candidates. This approach **mimics decision-making** processes, with the model evaluating and selecting the most promising options. ## Beam Search Step-by-Step Consider a standard sequence-to-sequence model, represented by the simple network below: ![Image by the author.](../images/001.png) Image by the author. At each timestep _t_ , the model outputs a single word, used to compose the final sequence. The final sequence will be nothing other than the provided translated sentence. If you are not familiar with RNNs, I suggest to check the simple introduction I included in this article: > [**Use Deep Learning to Generate Fantasy Names: Build a Language Model from Scratch**](https://towardsdatascience.com/use-deep-learning-to-generate-fantasy-character-names-build-a-language-model-from-scratch-792b13629efa) For text translation, we commonly use an [**encoder**](https://en.wikipedia.org/wiki/Autoencoder), which is a portion of the network dedicated to converting the input sequence into a vectorial form. In this case, the input sequence is the Spanish sentence to be translated. The encoder is followed by a [**decoder**](https://towardsdatascience.com/understanding-encoder-decoder-sequence-to-sequence-model-679e04af4346) which, at each timestep, returns a piece of the output sequence. ![Image by the author.](../images/002.png) Image by the author. Let's check the first step out in detail. The encoder (green part) receives the Spanish sentence "_Pablo estará en Nueva York la próxima semana._ " and provides a vector form of it to the decoder (blue part). ![Image by the author.](../images/003.png) Image by the author. At timestep 1, the decoder will output the first word of the translation. The key question is: > How does the decoder choose the word? The decoder chooses the most probable word, given the input sequence _x_. In other words, for each word in the dictionary, the model computes the corresponding probability to be the first word of the output sequence. ![](../images/body-0e2bf15290eb584a854f.png) The one word that maximizes this probability is chosen. Here is where **Beam Search enters the game** : it introduces a parameter _B_ , called **beam width** , which represents the number of words chosen by the model at each step. By setting, for instance, _B=3_ , the model will return not only one but three candidate words. Let's suppose the most probable first words of the translation are "_Pablo_ ", "_Next_ ", and "_During_ ". ![Image by the author.](../images/004.png) Image by the author. For each of these 3 candidate words, the model proceeds by guessing the second word _y²_ in the English translation. ![Image by the author.](../images/005.png) Image by the author. As always, the model picks the words that maximize a given probability. In this case: ![](../images/body-1d8b3d0c26e99025cf6c.png) You can clearly see the reason for the name Beam Search now. Starting from a narrow set of 3 candidates, the algorithm will enlarge it at each step, considering the probabilities of all these combinations. The output of step 2 consists of 9 candidate sequences: "_Pablo will_ ", "_Pablo is_ ", ..., "_During next_ ", and "_During one_ ". The model associates each one of them with a probability. By setting the parameter _B=3_ , the model will keep only the three candidate sequences with the higher probability. Suppose that the candidates with higher probabilities are "_Pablo will_ ", "_Pablo is_ ", and "_Next week_ ". During the 3-rd step, the decoder will guess 3 subsequent word for each candidate. ![Image by the author.](../images/006.png) Image by the author. The same process is iterated all over again until the model predicts as the most likely word the **End of Sentence** token. The [EOS](https://forum.opennmt.net/t/end-and-start-tokens/4570) token states that the sequence reached its end and it is now considered complete. ## Beam Search Variants Beam Search adaptability can be enhanced by several variants of the algorithm. They provide options that may be more or less adequate for diverse sequence generation tasks. The most common variants are: * **Length-Normalized Beam Search** : It adjusts for sentence length disparities by normalizing the probability scores, mitigating biases towards shorter sequences. * **Diverse Beam Search** : It introduces diversity in candidate selection to prevent the algorithm from converging prematurely, promoting exploration of a broader solution space. * **Top-**_**k**_**Beam Search** : It restricts the number of candidates considered at each step to the top-_k_ most probable, offering computational efficiency without compromising performance. * **Alpha-Diverse Beam Search** : It controls the trade-off between diversity and optimization by introducing an alpha parameter, enabling fine-tuning based on specific task requirements. * **Nucleus Sampling** : It defines a probability threshold (nucleus) and samples from the set of most probable candidates, enhancing the algorithm's focus on high-quality sequences. ![Image source: unsplash.com.](../images/007.jpeg) Image source: [unsplash.com](https://unsplash.com/photos/multicolored-light-passage-in-dark-area-d3Zu34NBg7A). ## Conclusion In summary, in this post we understood why Beam Search is the **most used algorithm** in the field of sequence generation, particularly in the context of **language translation** models, but also for **chatbot responses**. Its systematic exploration of multiple possibilities in parallel, guided by a defined beam width, enhances the precision and the contextualize of the generated sequences. I am confident that the step-by-step example I provided will make the abstract concept of Beam Search more concrete. As I always do in my posts, I will now recap some pros and cons of Beam Search. The main advantages of this algorithm are: * **Precision** : Beam Search excels in generating precise and contextually relevant sequences. * **Flexibility** : Variants like Diverse Beam Search and Top-_k_ Beam Search offer adaptability to diverse task requirements. * **Efficiency** : It efficiently explores a solution space, balancing computational complexity with performance. However, in Machine Learning there are **no free meals**. It is then important to clarify the drawbacks of each algorithm. In the case of Beam Search, I identified these possible issues: * **Computational Cost** : The exhaustive exploration of possibilities can incur computational overhead. * **Potential Convergence** : In standard configurations, Beam Search might converge prematurely to suboptimal solutions. ![Image by the author.](../images/008.png) Image by the author. Finally, I invite you to explore the provided resources to get a deeper understanding of this algorithm. * * * If you liked this story, consider following me to be notified of my upcoming projects and articles! ## References * [Deep Learning for Natural Language Processing, Brownlee, J. (2019)](https://oku.ozturkibrahim.com/docs_python/Deep_Learning_for_Natural_Language_Processing.pdf) * [Long short-term memory, Hochreiter, S., & Schmidhuber, J. (1997)](https://ieeexplore.ieee.org/abstract/document/6795963/) * [Neural Turing Machines, Graves, A., et al. (2014)](https://arxiv.org/abs/1410.5401) * [A Diversity-Promoting Objective Function for Neural Conversation Models, Li, J., et al. (2016)](https://aclanthology.org/N16-1014) * [Neural Machine Translation by Jointly Learning to Align and Translate, Bahdanau, D., et al. (2015)](https://arxiv.org/abs/1409.0473) ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-34347b17fbf8612c4a0c.jpeg) Written By ## [Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/) [See all from Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/) [Algorithms](https://towardsdatascience.com/tag/algorithms/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Programming](https://towardsdatascience.com/tag/programming/)[Sequence Model](https://towardsdatascience.com/tag/sequence-model/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fbeam-search-the-most-used-algorithm-in-sequence-models-107d56b23556%2F&title=Beam%20Search%3A%20the%20Most%20Used%20Algorithm%20in%20Sequence%20Models) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fbeam-search-the-most-used-algorithm-in-sequence-models-107d56b23556%2F&title=Beam%20Search%3A%20the%20Most%20Used%20Algorithm%20in%20Sequence%20Models) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fbeam-search-the-most-used-algorithm-in-sequence-models-107d56b23556%2F&text=Beam%20Search%3A%20the%20Most%20Used%20Algorithm%20in%20Sequence%20Models) Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. [Write for TDS](https://towardsdatascience.com/submissions/) ## Related Articles * [![](../images/009.jpg)](https://towardsdatascience.com/pythonic-tips-tricks-working-with-lambda-987444d80517/) ## [Pythonic Tips & Tricks - Working with Lambda](https://towardsdatascience.com/pythonic-tips-tricks-working-with-lambda-987444d80517/) [Programming](https://towardsdatascience.com/category/programming/) How to apply the Lambda function in Python [Tonichi Edeza](https://towardsdatascience.com/author/tonichi-edeza/) February 9, 20213 min read * [![](../images/010.jpeg)](https://towardsdatascience.com/ultimate-setup-for-your-next-python-project-179bda8a7c2c/) ## [Ultimate Setup for Your Next Python Project](https://towardsdatascience.com/ultimate-setup-for-your-next-python-project-179bda8a7c2c/) [Programming](https://towardsdatascience.com/category/programming/) Starting any project from scratch can be daunting task... But not if you have this ultimate Python project blueprint! [Martin Heinz](https://towardsdatascience.com/author/martin.heinz/) January 15, 20208 min read * [![](../images/011.png)](https://towardsdatascience.com/web-based-database-powered-chatbot-project-module-1-approximate-string-matching-65df0d8c999d/) ## [Web-Based Database-Powered Chatbot Project - Module 1: Approximate String Matching](https://towardsdatascience.com/web-based-database-powered-chatbot-project-module-1-approximate-string-matching-65df0d8c999d/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) A project spanning web programming, math, AI, and more. [Luciano Abriata](https://towardsdatascience.com/author/lucianosphere/) May 10, 202213 min read * [![](../images/012.jpg)](https://towardsdatascience.com/31-uniques-python-packages-to-improve-your-data-workflow-4f9762fc8f8b/) ## [31 Uniques Python Packages To Improve Your Data Workflow](https://towardsdatascience.com/31-uniques-python-packages-to-improve-your-data-workflow-4f9762fc8f8b/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) Various Python packages for data people [Cornellius Wijaya](https://towardsdatascience.com/author/cornelliusyudhawijaya/) July 26, 202217 min read * [![](../images/013.png)](https://towardsdatascience.com/kernel-case-study-flash-attention/) ## [Kernel Case Study: Flash Attention](https://towardsdatascience.com/kernel-case-study-flash-attention/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Understanding all versions of flash attention through a triton implementation [Arunjith A](https://towardsdatascience.com/author/arunjith.a/) April 3, 202514 min read * [![](../images/014.jpg)](https://towardsdatascience.com/efficient-machine-learning-why-you-should-think-about-the-customer-in-your-algorithm-1b2ec591ade3/) ## [Efficient Machine Learning - Why You Should Think About the "Customer" in Your Algorithm](https://towardsdatascience.com/efficient-machine-learning-why-you-should-think-about-the-customer-in-your-algorithm-1b2ec591ade3/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) It's easy to get caught up in building a lovely new machine learning algorithm. You see one network design, then another, and you keep... [Kabir Nagrecha](https://towardsdatascience.com/author/kabirnagrecha/) April 9, 20214 min read * [![](../images/015.png)](https://towardsdatascience.com/tinybert-1a928ba3082b/) ## [Large Language Models: TinyBERT - Distilling BERT for NLP](https://towardsdatascience.com/tinybert-1a928ba3082b/) [Large Language Models](https://towardsdatascience.com/category/artificial-intelligence/large-language-models/) Unlocking the power of Transformer distillation in LLMs [Slava Efimov](https://towardsdatascience.com/author/slavahead/) October 21, 20238 min read * [![](../images/016.jpg)](https://towardsdatascience.com/learn-to-use-the-seven-most-crucial-python-libraries-for-data-science-c5bc8dcffdd4/) ## [Learn to use the seven most crucial python libraries for data science](https://towardsdatascience.com/learn-to-use-the-seven-most-crucial-python-libraries-for-data-science-c5bc8dcffdd4/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) As a data scientist, these libraries will save your life while implementing ML/DL algorithms. [Harsh Maheshwari](https://towardsdatascience.com/author/anveenaik/) November 25, 20215 min read * [![](../images/017.png)](https://towardsdatascience.com/shell-language-processing-machine-learning-for-security-intrusion-detection-with-linux-auditd-73d7196995c7/) ## [Shell Language Processing: Machine Learning for Security Intrusion Detection with Linux auditd](https://towardsdatascience.com/shell-language-processing-machine-learning-for-security-intrusion-detection-with-linux-auditd-73d7196995c7/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) How-to guide with code samples for specialists willing to apply data science techniques for solving cyber-security problems. [Dmitrijs Trizna](https://towardsdatascience.com/author/ditrizna/) September 22, 202211 min read * [YouTube](https://www.youtube.com/c/TowardsDataScience) * [X](https://x.com/TDataScience) * [LinkedIn](https://www.linkedin.com/company/towards-data-science/) * [Threads](https://www.threads.net/@towardsdatascience) * [Bluesky](https://bsky.app/profile/towardsdatascience.com) [](https://towardsdatascience.com/) Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/) © Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network) #### Recommended Articles Close * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ "How Relevance Models Foreshadowed Transformers for NLP") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ "Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ "The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ "Does Your Company Have a Data Strategy?")