Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining. The formula of logistic regression is to apply a sigmoid function to the output of a linear function. This article …
Read more “Building a Logistic Regression Classifier in PyTorch”
Posted by Harsh Mehta, Software Engineer, and Walid Krichene, Research Scientist, Google Research Large deep learning models are becoming the workhorse of a variety of critical machine learning (ML) tasks. However, it has been shown that without any protection it is plausible for bad actors to attack a variety of models, across modalities, to reveal …
Read more “Leveraging transfer learning for large scale differentially private image classification”
Predictive maintenance is a data-driven maintenance strategy for monitoring industrial assets in order to detect anomalies in equipment operations and health that could lead to equipment failures. Through proactive monitoring of an asset’s condition, maintenance personnel can be alerted before issues occur, thereby avoiding costly unplanned downtime, which in turn leads to an increase in …
Organizations worldwide are excited about the potential of Artificial Intelligence and Machine Learning capabilities. However, according to HBR, only 20% see their ML models go into production because ML often is deployed separately from their core data analytics environment. To bridge this increasing gap between data and AI, organizations need to build massive data pipelines, …
Read more “Announcing new BigQuery inference engine to bring ML closer to your data”
Cloud-based coding platform Replit partners with Google Cloud to provide access to Google’s AI and infrastructure — and challenge Microsoft.Read More
Researchers found that preschoolers prefer learning from what they perceive as a competent robot over an incompetent human. This study is the first to use both a human speaker and a robot to see if children deem social affiliation and similarity more important than competency when choosing which source to trust and learn from.
Probabilistic machine learning methods are becoming increasingly powerful tools in data analysis, informing a range of critical decisions across disciplines and applications, from forecasting election results to predicting the impact of microloans on addressing poverty.
Last Updated on March 28, 2023 In the previous session of our PyTorch series, we demonstrated how badly initialized weights can impact the accuracy of a classification model when mean square error (MSE) loss is used. We noticed that the model didn’t converge during training and its accuracy was also significantly reduced. In the following, …
Read more “Training Logistic Regression with Cross-Entropy Loss in PyTorch”
The neural transducer is an end-to-end model for automatic speech recognition (ASR). While the model is well-suited for streaming ASR, the training process remains challenging. During training, the memory requirements may quickly exceed the capacity of state-of-the-art GPUs, limiting batch size and sequence lengths. In this work, we analyze the time and space complexity of …
Read more “Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation”
(Scroll down for English translation below) W przestrzeni publicznej pojawia się wiele pytań dotyczących działalności Palantira. Biorąc pod uwagę duże zainteresowanie naszą firmą, zebraliśmy pytania, które stawiane są najczęściej i tym wpisem na blogu, chcielibyśmy na nie odpowiedzieć. Czym jest Palantir? Palantir Technologies to międzynarodowa firma technologiczna założona w 2003 roku w Dolinie Krzemowej. Obecnie …