Elevenlabs v3 is sick
This’s going to change the face how audiobooks are made. Hope opensource models catch this up soon! submitted by /u/pheonis2 [link] [comments]
This’s going to change the face how audiobooks are made. Hope opensource models catch this up soon! submitted by /u/pheonis2 [link] [comments]
Machine learning is not just about building models.
This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services and solutions to align strategically with each brand’s unique needs. Radial …
Today, we are excited to announce that Gartner® has named Google as a Leader in the 2025 Magic Quadrant™ for Data Science and Machine Learning Platforms report (DSML). We believe that this recognition is a reflection of continued innovations to address the needs of data science and machine learning teams, as well as new types …
Google said the newest version of Gemini 2.5 Pro, now on preview, gives faster and more creative responses while performing better than OpenAI’s o3.Read More
As Elon Musk and US president Trump spar on social media, tech investors and executives are being forced to choose whether to support the most powerful man in business—or the White House.
Given the recent explosion of large language models (LLMs) that can make convincingly human-like statements, it makes sense that there’s been a deepened focus on developing the models to be able to explain how they make decisions. But how can we be sure that what they’re saying is the truth?
I’ve been active on this sub basically since SD 1.5, and whenever something new comes out that ranges from “doesn’t totally suck” to “Amazing,” it gets wall to wall threads blanketing the entire sub during what I’ve come to view as a new model “Honeymoon” phase. All a model needs to get this kind of …
Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.
Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuning foundation models on one task often leads to the issue of concept forgetting on other tasks. Recent methods of robust fine-tuning aim to mitigate forgetting of prior knowledge without affecting the …