Resource of free step by step video how to guides to get you started with machine learning.
Monday, June 1, 2020
DeepMind x UCL | Deep Learning Lectures | 4/12 | Advanced Models for Computer Vision
Following on from the previous lecture, DeepMind research scientist Viorica Patraucean introduces classic computer vision tasks beyond image classification (object detection, semantic segmentation, optical flow estimation) and describes state of the art models for each, together with standard benchmarks. She discusses similar models for video processing for tasks like action recognition, tracking, and the associated challenges. In particular, she refers to recent work to make video processing more efficient, including using elements of reinforcement learning. Next, she describes various settings for self-supervised learning in uni-modal and multi-modal (vision+audio, visio+language) settings, where large scale is beneficial. Viorica ends with a discussion on open questions in vision and the role of computer vision research within the broader goal of building intelligent agents. Speaker Bio: Viorica is a Research Scientist at DeepMind, working mainly on Computer Vision related problems, with focus on video processing. She did her PhD in Toulouse, France, on statistical models for image processing, and then focused on 3D shape- and video-analysis during her postdoctoral work in Paris and Cambridge. Her dream is to contribute to creating a computational model for the human visual system. About the lecture series: The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning. In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation. Find out more about how DeepMind increases access to science here: https://ift.tt/3dnjF7D
Labels:
Video
Published by Free artificial intelligence and machine learning video tutorial resource
Related video tutorials
Subscribe to:
Post Comments (Atom)
Most watched
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: For machine-learning work, buy an external SSD over a spinning hard drive, pick USB 3.2 Gen 2 (10 Gbps) or faster, and size f...
-
Quick answer: Yes, for most budget builders: a used RTX 3090 is still the cheapest route to 24 GB of VRAM with full CUDA support. Capacity...
-
This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Grad...
-
In this video, you'll learn how to use machine learning, computer vision and deep learning to create a football analysis system. This pr...
No comments:
Post a Comment