Which AWS SageMaker Algorithm should I Choose?! A Guide to Available AWS Built-in Algorithms
In this video, we will provide a comprehensive summary of available AWS SageMaker Built-in algorithms. If you like this video, check out this full course on Udemy: https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/?referralCode=DF6E7628ADBE1E0E38F4 Available SageMaker Algorithms: - BlazingText Word2Vec: BlazingText implementation of the Word2Vec algorithm for scaling and accelerating the generation of word embeddings from a large number of documents. - DeepAR: An algorithm that generates accurate forecasts by learning patterns from many related time-series using recurrent neural networks (RNN). - Factorization Machines: A model with the ability to estimate all of the interactions between features even with a very small amount of data. - Gradient Boosted Trees (XGBoost): Short for “Extreme Gradient Boosting”, XGBoost is an optimized distributed gradient boosting library. - Image Classification (ResNet): A popular neural network for developing image classification systems. - IP Insights: An algorithm to detect malicious users or learn to usage patterns of IP addresses. - K-Means Clustering: One of the simplest ML algorithms. It’s used to find groups within unlabeled data. - K-Nearest Neighbor (k-NN): An index based algorithm to address classification and regression based problems. - Latent Dirichlet Allocation (LDA): A model that is well suited to automatically discovering the main topics present in a set of text files. - Linear Learner (Classification): Linear classification uses an object’s characteristics to identify the appropriate group that it belongs to. - Linear Learner (Regression): Linear regression is used to predict the linear relationship between two variables. - Neural Topic Modelling (NTM): A neural network based approach for learning topics from text and image datasets. - Object2Vec: A neural-embedding algorithm to compute nearest neighbors and to visualize natural clusters. - Object Detection: Detects, classifies, and places bounding boxes around multiple objects in an image. - Principal Component Analysis (PCA): Often used in data pre-processing, this algorithm takes a table or matrix of many features and reduces it to a smaller number of representative features. - Random Cut Forest: An unsupervised machine learning algorithm for anomaly detection. - Semantic Segmentation: Partitions an image to identify places of interest by assigning a label to the individual pixels of the image. - Seqence2Sequence: A general-purpose encoder-decoder for text that is often used for machine translation, text summarization, etc. I hope you liked this video! Thanks #sagemaker #aws
In this video, we will provide a comprehensive summary of available AWS SageMaker Built-in algorithms. If you like this video, check out this full course on Udemy: https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/?referralCode=DF6E7628ADBE1E0E38F4 Available SageMaker Algorithms: - BlazingText Word2Vec: BlazingText implementation of the Word2Vec algorithm for scaling and accelerating the generation of word embeddings from a large number of documents. - DeepAR: An algorithm that generates accurate forecasts by learning patterns from many related time-series using recurrent neural networks (RNN). - Factorization Machines: A model with the ability to estimate all of the interactions between features even with a very small amount of data. - Gradient Boosted Trees (XGBoost): Short for “Extreme Gradient Boosting”, XGBoost is an optimized distributed gradient boosting library. - Image Classification (ResNet): A popular neural network for developing image classification systems. - IP Insights: An algorithm to detect malicious users or learn to usage patterns of IP addresses. - K-Means Clustering: One of the simplest ML algorithms. It’s used to find groups within unlabeled data. - K-Nearest Neighbor (k-NN): An index based algorithm to address classification and regression based problems. - Latent Dirichlet Allocation (LDA): A model that is well suited to automatically discovering the main topics present in a set of text files. - Linear Learner (Classification): Linear classification uses an object’s characteristics to identify the appropriate group that it belongs to. - Linear Learner (Regression): Linear regression is used to predict the linear relationship between two variables. - Neural Topic Modelling (NTM): A neural network based approach for learning topics from text and image datasets. - Object2Vec: A neural-embedding algorithm to compute nearest neighbors and to visualize natural clusters. - Object Detection: Detects, classifies, and places bounding boxes around multiple objects in an image. - Principal Component Analysis (PCA): Often used in data pre-processing, this algorithm takes a table or matrix of many features and reduces it to a smaller number of representative features. - Random Cut Forest: An unsupervised machine learning algorithm for anomaly detection. - Semantic Segmentation: Partitions an image to identify places of interest by assigning a label to the individual pixels of the image. - Seqence2Sequence: A general-purpose encoder-decoder for text that is often used for machine translation, text summarization, etc. I hope you liked this video! Thanks #sagemaker #aws