ML Fundamentals

Research talk: Causal learning: Discovering causal relations for out-of-distribution generalization

Keynote: Unlocking exabytes of training data through privacy preserving machine learning

JAX MD: A Framework for Differentiable Atomistic Physics

Does Model Performance Even Matter?

Masters in Financial Economics | Oxford Saïd Business School

Bike Share Demand Forecasting

The Not So Talked About Reasons Model Monitoring Fails // Oren Razon // MLOps Meetup #88

Parameter Prediction for Unseen Deep Architectures (w/ First Author Boris Knyazev)

Oxford University Centre for Corporate Reputation - Virtual Reputation Symposium 2021

Github Copilot: Good or Bad?

Sentdex Live: GTC Keynote News and free GPUs

Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525

Talks S2E5 (Luca Massaron): Hacking Bayesian Optimization

Applications of Variational Autoencoders and Bayesian Optimization w/ J. M. Hernández Lobato - #510

Model serving platform // Kyle Gallatin // Coffee #43 short clip

How new libraries can improve model performance with OctoML's Luis Ceze

Detecting Drift

Cloud Computing Expert Kesha Williams on Hiring, Mentoring, & Creating Community in Tech

Beyond Technical Acumen: Kaggle’s CEO on the Key Elements of a Data Scientist Skill Set

Machine learning for Accessibility | Session

Does your app use ML? Make it a product with TFX | Session

Using a TensorFlow Python MIRNet model in Node.js - Made with TensorFlow.js

Advanced Developer Workloads with Built-In AI Acceleration

Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants

The Discovery That Transformed Pi

Building World-Class NLP Models with Transformers and Hugging Face | Grandmaster Series E4

Opening remarks: Cloud Intelligence/AIOps

Research talk: Computationally efficient large-scale AI

Research talk: Towards data-efficient machine learning with meta-learning

Panel: Computer vision in the next decade: Deeper or broader

Panel: Causal ML Research at Microsoft

Talk: The implicit bias of optimization algorithms in deep learning

Talk: Theoretical Aspects of Gradient Methods in Deep Learning

A law of robustness and the importance of overparametrization in deep learning

Can we simulate a real robot?

Spatiotemporal Data Analysis with Rose Yu - #508

Spence Green — Enterprise-scale Machine Translation

Introducing Retiarii: A deep learning exploratory-training framework on NNI

Luis Ceze — Accelerating Machine Learning Systems

CI/CD for MLOPS Definition // Monmayuri Ray // MLOps Coffee Sessions #41 short clip

Phil Brown — How IPUs are Advancing Machine Intelligence

Probabilistic Numeric CNNs with Roberto Bondesan - #482

Knowledge Distillation as Semiparametric Inference

Sound Capture and Speech Enhancement for Communication and Distant Speech Recognition

Self-Tuning Networks: Amortizing the Hypergradient Computation for Hyperparameter Optimization

Oxford High Performance Leadership Programme | Ethos and Virtual Design

Project InnerEye: Augmenting cancer radiotherapy workflows with deep learning and open source

Directions in ML: Taking Advantage of Randomness in Expensive Optimization Problems