87 Stories To Learn About Machinelearning

Let's learn about via these 87 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Machinelearning /Learn Repo 1. Bitcoin Cash vs. Bitcoin SV: What Data Tells Us 10 Months After the Hash Wars November 2018 marked another pivotal moment for the crypto industry with the creation of Bitcoin Satoshi Vision(BSV) which quickly rose to the top 10 cryptocurrencies by market cap. The new crypto-asset was the result of a bitter technical and ideological battle between two factions of the Bitcoin Cash(BCH) community which ended up in one of the most aggressive hard forks in the history of crypto markets. During that time, both groups drew passionate and very vocal followers which certainly helped BSV gain certain prominence. However, ten months after the hard-fork, blockchain datasets reveal some very interesting insights about the health of both BCH and BSV. The IntoTheBlock platform recently added full support for BSV and I thought it would be a good idea to look at the data in comparison to BCH. 2. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us. 3. Crystal Ball Gazing: A Peek Into a World Ruled by Artificial Intelligence Oh this word has become such a norm today that no matter how fast you try to run away from it, this buzzword will eventually find you. Artificial Intelligence is such a common practice today, it is hard to stay away from it. The technological world, directly or indirectly, revolves around AI. It is either Artificial Intelligence or Machine Learning, it has to be this! 4. Indoor Positioning and Predicting the Most Suitable Boutiques in Shopping Malls for Customers Indoor navigation and machine learning combination both for helping users to find the most suitable stores and for helping stores to advertise their products. 5. 8 Use Cases for Voice Cloning with Artificial Intelligence If you thought that voice cloning and deepfakes are recent buzzwords, think again. The first original record of mimicking human voice dates back to 1779, in Russia. Professor Christian Kratzenstein built acoustic resonators that mimicked the human vocal tract when activated by means of vibrating reeds (just like wind instruments), in his lab in St. Petersburg. 6. Auto-Generating Lyrics With TensorFlow and Machine Learning: A How-To Guide Creating a bot that, given a starting phrase, would generate its own lyrics, powered by a machine learning model that would have learned from existing songs. 7. How I Built a Demo App to Listen to 5000+ Hours of Joe Rogan With the Help of AI I’m consuming 5500+ hours of Joe Rogan with the help of AI 8. The Rise of MLOps: What We Can All Learn from DevOps The MLOps Conference took place earlier this week at Hudson Mercantile in New York City. Experts from the New York Times, Twitter, Netflix and Iguazio, the host company, spoke about best practices and machine learning implementation throughout a variety of different organizations. 9. Say Goodbye to SEO - ChatGPT Steals the Show With Smarter Search Search Engine Optimization (SEO) has been the backbone of an online search for over two decades now. But as Artificial Intelligence (AI) technology moves quickl 10. An Introduction to “Liquid” Neural Networks Liquid neural networks are capable of adapting their underlying behavior during the training phase. 11. A Peek into the Future of Higher Education - Can Artificial Intelligence Drive Remote Learning? Artificial Intelligence is already having a profound effect on the world around us. As cloud-based computing and Big Data analytics bring unprecedented convenience to our daily lives and countless industries, it’s worth taking a moment to consider how machines can help in the field of education in years to come, considering that revenues from the AI software market is projected to reach almost $120 billion by 2025. 12. Artificial Intelligence and the Future of Humans Artificial Intelligence is in many ways reshaping our tools and human-based methods, from the medical field to everyday gadgets and entertainment, to outer space. Humans are relying on AI more and more every day. 13. Generative Adversarial Networks (GANs): An Overview GAN or Generative Adversarial Network is one of the most fascinating inventions in the field of AI. All the amazing news articles we come across every day, related to machines achieving splendid human-like tasks, are mostly the work of GANs! 14. 5 Most Common Chatbot Mistakes made by eCommerce Websites - and How to Avoid Them Artificial intelligence, machine learning, and chatbots are on everyone’s lips in the eCommerce industry. These new technologies are shaking things up and changing the way we do business online so it’s no surprise that, by 2020, 80% of businesses are projected to have chatbot automation software enabled. 15. Using Flask to Build a Rule-based Chatbot in Python Learn to build AI ruled-based chatbot with a simple tutorial that can be showcased in your Portfolio. 16. GPU Computing for Machine Learning By taking advantage of the parallel computing capabilities of GPUs, a significant decrease in computational time can be achieved relative to traditional CPU 17. Learning by Producing: Generative Adversarial Networks The machines have been trying to learn to recognize and identify the photos they have seen for years. In 2013, it succeeded in reaching the human level. Machine learning systems have provided simple output from a complex input. It can detect almost all details of a photos and display users exactly want they want. 18. 8 Best Python Libraries For Machine Learning in 2021 🛠 Numpy, Scipi, Keras, and Theano are some of the best Python Libraries For Machine Learning in 2021. 19. Kinetics Dataset - Training and Evaluating Models for Video Classification A guide to using the open-source tool FiftyOne to download the Kinetics dataset and evaluate video understanding models 20. The Future of Human In The Loop Since the 1980’s, human/machine interactions, and human-in-the-loop (HTL) scenarios in particular, have been systematically studied. It was often predicted that with an increase in automation, less human-machine interaction would be needed over time. Human input is still relied upon for most common forms of AI/ML training, and often even more human insight is required than ever before. 21. Behavioral Signals Analyzes Human Behavior from Voice Data Behavioral Signals is an AI company that develops AI technology to analyze human behavior from voice data. 22. What's the Difference Between MLOps and AIOps An overview of the MLOps and AIOps worlds to understand what they mean, how they relate to DevOps, and how they compare in terms of benefits. 23. An Introduction to Adversarial Attacks and Defense Strategies Adversarial training was first introduced by Szegedy et al. and is currently the most popular technique of defense against adversarial attacks. 24. A Pleasant Way to Kick Off Your Data Science Education- This is CS50 So You Want to Get Into Data Science 25. Building a Propensity Model to Target Users Better in Marketing Campaigns Propensity model to figure out the likelihood of a person buying a product on their return visit. We need to identify the probability to convert for each user. 26. Content Recommendation and Cognitive Biases Artificial Intelligence (AI) is a fascinating invention of mankind. Fusing the computational power of a machine with the intellect of a human undoubtedly creates new possibilities of innovation and tremendously increases the likelihood of realizing those which were already conjectured. 27. Deepfake Autofiction: A New Wave of AI-generated Literature First, AI knows how to drive. Now, they know how to write. When are they going to learn to cut my lawn or add in morally dubious scenes into my movies? 28. Write Machine Learning Apps Faster With the OneML SDK Write faster ML apps using an optimized and portable machine learning SDK. 29. ChatGPT Answered 50,000 Trivia Questions - Here's How It Did I made ChatGPT answer 50,000 trivia questions. Find out what happens 30. Building A Chatbot On Your Own Might Not Make As Much Sense As You Think Over the past decade plus, chatbots have dominated the conversation (no pun intended) when it comes to digital engagement. You’ve undoubtedly had experiences interacting with them, some helpful while others underwhelming, and perhaps even fiddled around with building one on your own. 31. What You Need to Know About Netflix's 'Jupyter Killer': Polynote 📖 Today, Netflix open-sourced Polynote, the internal notebook they developed, to the public. It’s not rare these days that big tech companies open sources their internal tools or services, then got popular and adopted by the industry. Amazon AWS, Facebook’s React.js, etc. are two of them. It makes sense. These big tech companies have the best engineers in the industry and more often than not they are facing the biggest challenges that will drive the development of great tools. Netflix’s Polynote could be another one of those great tools and the data science/machine learning industry does need better tools in terms of how to write code, experiment algorithms and visualize data. Here are several things you need to know about this new tool. I’ll try to keep this succinct and to the point so you can quickly read through it and be knowledgeable about the pros and cons of this new choice of our development/research environment. 32. Frontend Dev: How to Build a Predictive Machine Learning Site With React and Python (Part 3) We will be building a machine learning React frontend that will predict whether a candidate will or will not be hired based on his or her credentials. 33. Using BERT Transformer with SpaCy3 to Train a Relation Extraction Model A step-by-step guide on how to train a relation extraction classifier using Transformer and spaCy3. 34. Softmax Temperature and Prediction Diversity This article is about tweaking the softmax distribution to control how diverse and novel the predictions are. 35. How To Get Started With Machine Learning: A Tutorial For Beginners By A Beginner I Learned Machine Learning in a Weekend, here's how I did it and the steps that I would recommend to take if you want to do the same! 36. Evaluating Regression Models in Machine Learning Model evaluation is very important since we need to understand how well our model is performing. 37. Artificial Intelligence: Stats and Facts You Should Know in 2020 TV shows and fiction aside, the present day examples of basic AI are Google Voice, Cortana, Alexa, Siri and chatbots. However, AI isn’t just limited to voice assistants, it’s turning tables in other domains and industries as well. Several restaurants for instance have bots for room service, serving food and carrying luggage. 38. Machine Learning Platform Collaboration Between Dell EMC and Comet [Partnership Announcement] Dell EMC, a leading provider of full-stack solutions for data science teams, and Comet, the industry-leading meta machine learning experimentation platform, announced a collaboration with a reference architecture for data science teams looking to harness the power of the Dell EMC infrastructure in tandem with Comet’s meta machine learning platform. 39. Machine Learning Solutions - Ensuring Powerful Cybersecurity for Modern-Day Companies Machine learning development services help in maintaining foolproof cybersecurity, supporting experts in fighting crime, and discouraging high-risk entities. 40. How No-Code Can Rekindle Your Relationship With Data Science A modern business user’s relationship with data is fairly complicated. It starts with curiosity. “Which of my top users will do X,Y, or Z?” You need a data output to move forward with a decision—except you’re having communication issues. 41. 7 AI-powered Chatbots If you’re a millennial, you’ll know SmarterChild, the first-ever instant messaging bot with natural language comprehension ability. It was developed in 2000 and demonstrated exceptional wit, which most of today’s bot cannot. SmarterChild used to chat with about 2,50,000 humans every day with funny, sad, and sarcastic emotions. Today, we’ve traveled a distance with technologies like AI, ML, NLP, etc. and bots like Xiaocle have passed Turing tests of 10 minutes (i.e. users couldn’t identify that they’re talking to a bot for about 10 minutes). 42. How to Run Machine-Learning Models in the Browser using ONNX Learn how to use ONNX Runtime Web to deploy machine-learning models natively to the browser. 43. Chatbots: The end of the hype era or a bright new dawn? We live in an age of great innovation and technological marvel. Artificial intelligence and machine-learning technologies are developing at an insanely fast pace, which resulted in the slightly increased performance of digital applications. A few years ago, chatbots formed a new, highly popular trend and have consistently been regarded as the best potential help for the labor market, slightly reducing the need for staff workload. The chatbot sector is now growing fast, and the total market is predicted to reach around 1.25 billion US dollars in 2025, a significant increase from the market size in 2016, which stood at $190.8 million. While becoming more sophisticated over the years, AI-driven bots are being used more often in various fields: marketing, healthcare, CRM [Customer Relationship Management]. This hypetrain resulted in more efficient solutions and brighter light at the end of the tunnel. 44. Naive Sentiment Analysis Using R Cleuton Sampaio, October 2019 45. Embeddings in Machine Learning: Everything You Need to Know Here's a deep dive into the history of machine learning embeddings, common uses, and current infrastructure solutions, including the vector database. 46. The Best (and Worst) Punny Jokes Only Data Scientists Will Understand For the first KDnuggets post on Hacker Noon, we bring you a lighter fare of very nerdy computer humor from the series of self-referential jokes started on Twitter earlier this week. Here are some of our favorites. If you do understand all of the jokes, then you congratulate yourself on having excellent knowledge of Data Science and Machine Learning! If you have actually laughed at 2 or more jokes, then you have earned MS in Computer Humor! If you just smirked, you probably have a Ph.D. And I have a great joke about AGI, but it will be ready in 10 years. Enjoy, and if you have more, add them in comments below! Yann LeCun, @ylecun 47. Applying Machine Learning to Crypto-Sphere: The Good and the Bad Aspects Anyone who has traded cryptocurrencies or invested in Bitcoin stocks before has been frustrated by the difficulty involved with trying to predict market trends. 48. How Companies Are Actually Using AI in Everyday Practices When thinking of AI (artificial intelligence), mixed emotions often come to mind. For movie buffs, we might immediately see images of Will Smith battling it out with humanoid AI creatures in IRobot or the even more realistic looking depiction of artificial intelligence in the movie aptly called AI. In our human minds, AI is something that could potentially lead to a catastrophic apocalypse as machines take over the world. 49. Building a Monte Carlo Markov Chain Pipeline Using Luigi A few months ago I was accepted into a data science bootcamp - Springboard, for their data science career track. As part of this bootcamp I had to work on Capstone projects that would help build my portfolio, show my ability to extract, clean up data, build models and extract insights from said models. For my first project I opted to build a Monte Carlo Markov Chain pipeline initially with the objective of building a multi-touch attribution model that would help me understand conversion rates from different states in the signup process and use that to understand which channels appeared to deliver the greatest conversion rates for users coming through a given landing page and transitioning through the different signup states defined in my dataset. 50. Computer Vision Could Improve Health and Workplace Safety Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. 51. ML Essentials: Top 10 Lists Every Data Scientist Should Know Data Science is no doubt the "sexiest" career path of the 21st century, made up of people with strong intellectual curiosity and technical expertise to dig out valuable insights from humongous volumes of data. This helps firms add value by improving their productivity, unlocking insights for better decision making and profit gains, just to mention a few. The knowledge of Data Science is desirable and useful across various industries. 52. Top 6 Applications of Natural Language Processing in Healthcare For many healthcare providers, the industry is shaping up to be more of a shifting quandary of regulatory issues, financial turmoil, and unforeseeable eruptions of resentment from practitioners on the edge of revolt. The industry is now taking the opportunity to scale up their big data defenses and develop the technological infrastructure required to meet the imminent challenges. 53. Why I Spent Years Writing a Children’s Book on Data Science I wrote a children's book on data science to inform others who have a hard time understanding data science and machine learning concepts, especially kids! 54. NATURAL LANGUAGE PROCESSING (NLP) | EXPLAINED Natural Language Processing (NLP) refers to AI method of communicating with intelligent systems using a natural language such as English. 55. 7 Competition-Killing Ways To Use Machine Learning for Ecommerce Brands Leave competitors in your ecommerce niche gasping for air with these machine learning tools that automate costs out and show you where your customers are hiding. 56. 8 Machine Learning Trends that Impact Business in 2021 and Beyond Let’s discover the latest innovations in machine learning in 2021-2022 and go over various examples of how this technology can benefit you and your business. 57. AI Will Be Used to Detect Masks Through CCTV Footage in India While the global pandemic has caused a lot of distress among workers and service providers, things are gradually getting better now. With services starting to work everywhere, the transportation industry has also bounced back. 58. What Are Convolution Neural Networks? [ELI5] Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn. 59. Rethinking Chatbots: They're Not Just for Customer Support Not so long ago, many industry watchers dismissed chatbots as an overhyped piece of programming. However, a number of businesses who backed it are reaping the rewards now. Chatbots are already solving a majority of customer service queries across industries and it is estimated that by 2020, they will be handling 80-85 percent of all customer support interactions. 60. AI for Noobs: How Amazon Alexa Works How Amazon Alexa AI processes and implements commands. 61. Vladimir Vapnik's New Learning Model Vladimir Vapnik recently gave a talk about a new theory of learning he is working on. 62. The Future of Mobile Applications Is Brighter With Machine Learning This article will explore machine learning and its impact on mobile apps. 63. Insights into Machine Learning Machine learning is a lot of strategies by which PCs settle on choices self-governing. Utilizing certain systems, PCs settle on choices by considering or recognizing designs in past records and afterward anticipating future events. Diverse sorts of expectations are conceivable, for example, about climate conditions and house costs. Aside from expectations, machines have figured out how to perceive faces in photos, and significantly sift through email spam. 64. Machine Learning Algorithms Explained Can you remember five examples of machine learning in real life? We share impressive examples of ML that we use every day that may not be obvious to you. 65. Football Data Analysis Using Machine Learning Models Can Potentially Boost Throw-Ins! “Can machine learning models help improve ball accuracy, precision and retention, leading to scoring after throw-ins? 66. Announcing ModelDB 2.0 release Since we wrote ModelDB 1.0, a pioneering model versioning system, we have learned a lot and adapting it to the evolving ecosystem became a challenge. Hence we decided to rebuild from the ground up to support a model versioning system tailored to make ML development and deployment reliable, safe, and reproducible. 67. Healthcare Technology Trends, Digital Innovations in 2022 Explore the main tech innovations that have the potential to transform your healthcare organization in 2022. 68. Jeremy Howard’s fast.ai vs Andrew Ng’s deeplearning.ai - Are They That Different From Each Other? How Not to ‘Overfit’ Your AI Learning by Taking Both fast.ai and deeplearning.ai courses 69. How To Build Links Detector That Making Links in Your Book Clickable How I built a link detector for your smart phone to browse links printed in books. 70. Predictive Early Stopping - A Meta Learning Approach Introduction 71. 8 ways in which AI helps the logistics industry The world of logistics has been completely transformed with the advent of newer technologies, especially AI, and it is not a luxury anymore but a necessity for a business in this industry to thrive. 72. Business perspective in the Future of AI development by 2025 “Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page (CEO of Alphabet) 73. How E-Commerce is Getting Smarter with Artificial Intelligence Artificial Intelligence (A.I.) is the future. 74. Training Machine Learning Models Using TensorFlow or PyTorch I will show you how gradient descent works, which is in the deepest deep of machine learning. [75. Differential Privacy with Tensorflow 2.0 : Multi class Text Classification Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh) Introduction 76. Machine Learning Explained in 5 Minutes Google uses it to provide millions of search results every hour. It helps Facebook guess your next love interest. Even Elon Musk’s Tesla uses it to make self-dr 77. Boost Your Customer Experience with Predictive Analytics In a world where product differentiators are minimal, customer experience is becoming the decisive factor. In a report by PwC, 73% of respondents listed customer experience as important, yet companies are still not leveraging this opportunity enough. Organizations should also consider that 42% of the same respondents said that they are ready to pay more if that guarantees a better experience. 78. Artificial Intelligence and Art: When Is Art Really Art? AI helpers can help artists create art, music, and even films. We are at the beginning of this journey, so let’s make sure we don’t set the bar too high. 79. Image Classification Model with Google AutoML [A How To Guide] In this tutorial, I'll show you how to create a single label classification model in Google AutoML. We'll be using a dataset of AI-generated faces from generated.photos. We'll be training our algorithm to determine whether a face is male or female. After that, we'll deploy our model to the cloud AND create the web browser version of the algorithm. 80. Deep Learning for Modeling Audio-Visual Correspondences Human perception is multidimensional and a balanced combination of hearing, vision, smell, touch, and taste. Recently, many pieces of research have tried to step forward on the road of improving machine perception by transitioning from single-modality learning to multimodality learning. 81. Top 30 Machine Learning Consulting Companies Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and the visualization by McKinsey Global Institute speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. 82. 10 helpful Python Tips and Tricks for Beginners In this post, we’d like to share 10 useful Python tips and tricks for beginners with you. 83. How to Win a Kaggle Competition: Box Office Prediction Competition Introduction 84. VOGUE by Google, MIT, and UW: The AI-Powered Online Fitting Room Google used a modified StyleGAN2 architecture to create an online fitting room where you can automatically try-on any pants or shirts you want using only an ima 85. Use Beta Distribution and Thompson Sampling to Beat The Multi-armed Bandit at the Casino As a logical person at the casino. you want to put your money on the machine with the maximum expected return. This is the origin of the multi-armed bandit problem. We will cover the two most basic concept here: Beta distribution and Thompson sampling. Beta Distribution 86. Intro to Audio Analysis: Recognizing Sounds Using Machine Learning 87. Detecting Medicare Provider Fraud with Machine Learning Medicare Healthcare Fraud Provider Prediction #hackernoon #xgboost #medicarefraud #machinelearning #streamlitapp #fraudprovider Thank you for checking out the 87 most read stories about Machinelearning on HackerNoon. to find the most read stories about any technology. 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