Algorithms MDPI’s Post

Excited to share my first Medium article on leveraging the power of neural networks for recommendation systems, inspired by YouTube's ranking algorithm! In this piece, I delve into how neural networks can revolutionize recommendation engines, enhancing user experience and engagement. Whether you're a data scientist, AI enthusiast, or simply curious about the future of personalized content recommendations, this article is for you. Read the full article here: https://lnkd.in/eS4x5T35 #NeuralNetworks #RecommendationSystems #AI #MachineLearning #Personalization #YouTubeAlgorithm #MediumArticle

YouTube's RecSys is a box of insights. What I love about their paper Deep Neural Networks for YouTube Recommendations, is the amount of thought they have put into designing a system instead of making the model fancier. Any ML engineer can leverage practical ideas like these. Hence, I have structured my latest article to reflect system/ops decisions instead of modeling details. I discuss ➡ Why structure recommendations as an extreme classification problem? ➡ Why the usual train-val split is not the best way to create a validation set? ➡ How to ensure the model does not exploit the platform's structure? ➡ How YouTube normalizes features for better convergence? ➡ How to think about the objective function for the model to optimize? ➡ How YouTube ensures its data sources are optimal for model learning. ... and more. If you think articles like these add value to your ML expertise, feel free to subscribe. Link to the article - https://lnkd.in/d3whmA9t

🌟 Excited to share the latest installment in my series on recommender systems! 🌟 In this article, we dive into Neural-Based Collaborative Filtering – a powerful approach that leverages deep learning to deliver highly personalized recommendations. 🧠 Discover how neural networks can capture complex user-item interactions. 📈 Learn about the architecture, training processes, and practical applications. 🔧 See how this advanced method compares to traditional techniques like memory-based collaborative filtering and matrix factorization. Check it out and let me know your thoughts! #MachineLearning #DeepLearning #RecommenderSystems #DataScience #AI #NeuralNetworks #Personalization

Harnessing the Power of Neural Networks: How Businesses are Gaining a Competitive Edge 🎉Exciting Announcement!🎉 I'm thrilled to share our latest blog post on "Harnessing the Power of Neural Networks: How Businesses are Gaining a Competitive Edge." 🚀 In today's data-driven world, businesses need innovative ways to stay ahead of the curve, and neural networks are the game-changers! 🧠💪 Discover how businesses are utilizing neural networks to make informed decisions, personalize customer experiences, optimize supply chains, detect fraud, and improve overall operational efficiency. This article breaks it all down for you! 🌐 Don't miss out on this insightful read! Check out the full blog post here: [Link](https://ift.tt/IeHk25S) Stay ahead of the competition with neural networks! 💡 #neuralnetworks #competitiveness #businessinsights #datadriven #blogpost https://ift.tt/IeHk25S

GANs consist of two neural networks i.e. Generator that generates a fake image of our currency note example and a discriminator that classifies it into real or fake. The generator’s role is to map the input to the desired data space (image as in the example above). On the other hand second neural network models i.e. the discriminator classify the output with probability as real or fake compared with real datasets. #AILabPage #VinsLens #VinsLines #neuralnetworks

In recent times, I have observed people using Neural networks for problems which can be easily solved using conventional machine learning models. Using neural networks for small problem use-cases that can be easily solved by other machine learning models might seem unnecessary or overkill at first glance. In the fascinating world of machine learning, it's essential to choose the right tool for the task at hand. Sometimes, the decision between simplicity and complexity can make all the difference in achieving accurate results. Imagine a real estate scenario where you're tasked with estimating a house's price based on its characteristics. Linear regression can provide clear guidance on how changes in square footage, number of bedrooms, and bathrooms directly influence the predicted selling price. The problem at hand involves a straightforward relationship between features and outcomes, linear regression shines. Its simplicity and interpretability make it an ideal choice for predicting outcomes that can be explained by a linear combination of inputs. ⭕ The Key takeaway I want you to take from this post is: When faced with a problem, take a step back to analyze its nature. Is it a problem with linear relationships, or does it involve intricate patterns? Choosing between linear regression and neural networks isn't about which is better, but which is best suited to tackle the specific challenge. 💡 Considerations: 🔹 Data Size: A limited dataset might favor linear regression, as simpler models require less data to generalize effectively. 🔸 Interpretability: Linear regression provides coefficients that directly show feature impact, while neural networks' inner workings can be more challenging to interpret. 🔹 Resource Demand: Neural networks demand more computational resources than linear regression. Simple problems might not require such complexity. Remember, as machine learning practitioners, our goal is to solve problems effectively and efficiently, and sometimes, that means leveraging simplicity or embracing complexity when needed. #machinelearning #decisionmaking #problemsolving #neuralnetworks #choosewisely

Exploring Machine Learning Algorithms: A Comprehensive Overview •Machine learning is a vast field, encompassing a wide range of algorithms and techniques. Here's a detailed breakdown tv guide your understanding: 1️⃣ Unsupervised Learning ▪️Clustering: K-Means, DBSCAN, Hierarchical Clustering, Gaussian Mixture Models (GMM) ▪️Dimensionality Reduction: PCA, SVD, ICA, t-SNE, LDA ▪️Association: Apriori Algorithm, ECLAT Algorithm, FP-Growth Algorithm 2️⃣ Supervised Learning ▪️Classification: K-Nearest Neighbors (KNN), Lvgistic Regressivn, Naive Bayes, Decision Trees, Support Vector Machines (SVM), Random Forest, Gradient Boosting Machines (GBM), Neural Networks (MLP, CNN) ▪️Regression: Linear Regression, Polynomial Regression, Ridge Regression, Lass Regression, Elastic Net, Support Vector Regression (SVR), Decision Trees, Random Forest, Gradient Boosting 3️⃣ Ensemble Learning ▪️Bagging: Random Forest, Bootstrap Aggregating (Bagging) ▪️Stacking: Stacked Generalization, Blending ▪️Boosting: Ada Boost, GBM, XGBoost, LightGBM, CatBoost 4️⃣ Neural Networks ▪️Feedforward Neural Networks: Multilayer Perceptron (MLP), Conventional Neural Networks (CNN) ▪️Recurrent Neural Networks: Lung Short-term Memory Networks (LSTM), Gated Recurrent Units (GRU) ▪️Generative Models: Generative Adversarial Networks (GAN), Variational AutoEncoders (VAE) ▪️Specialized Networks: Transformer Networks, Auto Encoders, Radial Basis Function Networks (RBFM) 5️⃣ Reinforcement Learning ▪️Value-Based: Q-Learning, Deep Q-Network (DQN) ▪️Policy-Based: REINFORCE Algorithm, Proximal Policy Optimization (PPO) ▪️Model-Based: AlphaZero, Dyna-Q Other Algorithms: Actor-Critic Methods as (A3C, A2C), Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic (SAC) Understanding these algorithms is crucial for anyone looking tv dive deep intv machine learning and its application. Each category offers unique approaches and tools for tackling different types of data and problems. Which vne are yvu must interested in exploring? Share your thoughts! #Machinelearning #DataScience #AI #Neural Networks #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #Data ScienceCommunity

Keen on apply more that just classic ML algorithms to your AI model? With ValueXI you can choose the suitable model training methods, and even add what's missing, applying custom modules. But first, let's see well-known Machine Learning Algorithm types: • Regression • Instance-based methods • Regularization methods • Decision tree • Bayesian • Kernel methods • Association Rule Learning • Artificial Neural Networks • Deep Learning • Dimensionality Reduction techniques • Ensemble Modelling With this as a base, you can get better results thanks to flexible tuning of data processing and training parameters. If you don't have the functionality you need, just add your own via custom modules in addition to the classic algorithms. Picture Credit: ScientistCafe (thanks a lot for putting everything together!) https://lnkd.in/ecrwuuAj #valuexi #aiandml #aicommunity #machinelearning #aidevelopment

Improving Reading Mode in Android and Chrome using on-device content distillation with graph neural networks #Ai #UseCase #NeuralNetworks

𝗚𝗡𝗡: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 (𝗣𝗮𝗿𝘁 𝗜) 💡 GNN: Graph Neural Network. Pictre yourself with a giant spiderweb of information, where each strand connects different pieces of data. That's kind of what a graph is – a bunch of stuff linked together in a web. Now, 𝗡𝗲���𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝘀𝘂𝗽𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗯𝘆 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗯𝗿𝗮𝗶𝗻, 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮. So, combining the two – graph data and neural networks – gives us GNNs! They can analyze how different pieces of data are connected and use that knowledge to make sense of the bigger picture. Here's a real-life example: imagine a social media network like Facebook. A GNN can look at how people are connected (friends, family, etc.) and use that to recommend posts or ads that you might be interested in. Pretty cool, right? 💡 𝗕𝘂𝘁 𝗵𝗼𝘄 𝗱𝗼 𝘁𝗵𝗲𝘀𝗲 𝗚𝗡𝗡𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸? Well, they break down the web of information into smaller chunks, one connection at a time. They learn the importance of each connection and how it influences the data at each point. Then, they use this knowledge to make predictions or answer questions about the entire network. 💡 𝗦𝗼, 𝘄𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗚𝗡𝗡𝘀? Well, they have the potential to revolutionize many fields. They can be used for things like: --- Fraud detection in financial networks --- Understanding the spread of diseases in biological networks --- Recommender systems (recommending products, movies, or music to users based on their preferences and their relationships with other users) The possibilities are endless! 𝗡𝗲𝘂𝗿𝗮𝗹 𝗴𝗿𝗮𝗽𝗵𝘀 𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝘀𝗲𝗱 𝗼𝗳 𝘁𝘄𝗼 𝗺𝗮𝗶𝗻 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀: 📊 𝗔 𝗴𝗿𝗮𝗽𝗵: It represents the entities(people, objects, events...) and their relationships (friendship, collaboration, similarity) 🧠 𝗔 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸: It is applied to the graph to learn the underlying patterns in the data. The neural network learns to assign weights to the relationships between entities, which allows it to make predictions about new data. 💡 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗻𝗲𝘂𝗿𝗮𝗹 𝗴𝗿𝗮𝗽𝗵𝘀 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: They can model complex and multidimensional relationships between entities, making them well-suited for tasks. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: The weights assigned to relationships by the neural network can be interpreted, which allows for a better understanding of the patterns. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Neural graphs can be applied to large datasets, making them suitable for Big Data applications. #datascience #GNN #AI #machinelearning #edulearnia #neuralnetwork #programminglanguages #bigdata #onlinecourses