Symbolica AI的动态

?? New Article, LinkedIn Community! ?? I'm thrilled to share my latest article on Medium: "Navigating Machine Learning Datasets: Test, Validation, and Training" ??. Read it here: https://lnkd.in/dmR7eRcF In this piece, I dive deep into machine learning datasets and explore their fascinating components. ??? If you're passionate about {AI (artificial intelligence or ML machine learning), I'd love for you to check it out and let me know your thoughts! ?? Your feedback and insights are invaluable to me. You can find this article and more of my work on my Medium blog: https://lnkd.in/d2xvUbFc ?? Thank you for your continuous support! ?? Your partner in code, Roscode ?? #machinelearning #ai #ml #datasets

Hello connections. Machine learning is a subset of artificial intelligence. In this post, I will talk about 3 components of machine learning. I explain each component including "what is structure and unstructured data", what are the algorithms in machine learning, and what is the most suitable machine learning algorithm for certain problems. #artificialintelligence #technical

The generative model is to learn the underlying distribution of the training data, allowing it to generate new data samples that are similar in structure and distribution to the original data.Unleash the power of Machine learning synthetic data in Ml field. #infocylanz #machinelearning #MLprompts

The generative model is to learn the underlying distribution of the training data, allowing it to generate new data samples that are similar in structure and distribution to the original data.Unleash the power of Machine learning synthetic data in Ml field. #infocylanz #machinelearning #MLprompts

Day 19 : Introduction to ML | ?? Ensemble Learning and Random Forest?? Ensemble learning is a technique in machine learning where multiple models are combined to produce better results than any single model. The three main methods are bagging, boosting, and stacking. To start with, bagging (Bootstrap Aggregating) creates several versions of a dataset by randomly sampling with replacement. It then trains a model on each version and averages their predictions to reduce overfitting. Moreover, boosting builds models sequentially, where each new model focuses on correcting the errors of the previous ones, making the overall model stronger and more accurate. Likewise, stacking combines different models by training a new model to find the best way to mix their predictions, often leading to better performance by capturing a wider range of patterns in the data. #30DaysMachineLearningChallenge #MachineLearning #DataScience #ArtificialIntelligence

The Power of Algorithm Efficiency in Machine Learning! Not all ML algorithms are equal! ?? Knowing runtime complexity can make or break your model's performance, especially with large datasets. Build smarter, faster, and cost-effective models with the right insights! #MachineLearning #DataScience #AlgorithmEfficiency #BigData #ModelOptimization #TechTips

Day 6: What are the main types of machine learning? There are three main types of ML: supervised (labeled data), unsupervised (finding patterns), and reinforcement (learning through rewards). #MLTypes #SupervisedLearning #UnsupervisedLearning #YourDataAnalyst

Next Up: Probability and Distribution for ML Q. Why P&D is important in ML? A. Probability distributions are essential tools for machine learning, as?they help describe the patterns and uncertainties of data and models. Onto a deeper dive into the world of ML! #MachineLearning #DataScience #ML

My latest presentation for the Croissant ML working group on the building distributed #AI based on Dataverse, Kaggle, Hugging Face and other data platforms, with LLM serving as "navigation interface" between data nodes in the network. https://lnkd.in/e8vUiWqY