The Fully Connected AI Conference London: May 15th , 2024
๐ซ Secure Your Spot: http://wandb.me/fclondon24
Experience the era of Generative AI at the Fully Connected 2024 conference, taking place at the Sancroft, Paternoster Square in London on May 15, 2024. Hosted by Weights & Biases, this premier event brings together the visionaries and builders of the AI world.
Prepare to be inspired by a lineup of expert speakers including:
Lukas Biewald, CEO, Weights & Biases
Shawn Lewis, CTO, Weights & Biases
Robin Bordoli, CMO, Weights & Biases
Industry leaders from the London Stock Exchange Group, BBC, Novo Nordisk, Volkswagen AG, and more!
The day is strโฆ
Watch on YouTube โ
(saves to browser)
Chapters (4)
12:00
PM: Registration
1:00
PM - 4:30 PM: Sessions on the latest in Generative AI, LLMOps, and industry appl
4:35
PM - 5:00 PM: Insightful panel on the future of Generative AI.
5:00
PM: Wrap up the day with a closing speech followed by a networking reception.
Playlist
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0. What is machine learning?
Weights & Biases
1. Build Your First Machine Learning Model
Weights & Biases
Intro to ML: Course Overview
Weights & Biases
2. Multi-Layer Perceptrons
Weights & Biases
3. Convolutional Neural Networks
Weights & Biases
Weights & Biases at OpenAI
Weights & Biases
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
4. Autoencoders
Weights & Biases
5. Sentiment Analysis
Weights & Biases
6. Recurrent Neural Networks [RNNs]
Weights & Biases
7. Text Generation using LSTMs and GRUs
Weights & Biases
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
Introducing Weights & Biases
Weights & Biases
10. Seq2Seq Models
Weights & Biases
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
Weights & Biases
14. Data Augmentation | Keras
Weights & Biases
15. Batch Size and Learning Rate in CNNs
Weights & Biases
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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Troubleshooting and Iterating ML Models with Lee Redden (2019)
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Designing a Machine Learning Project with Neal Khosla (2019)
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Lukas Beiwald on ML Tools and Experiment Management (2019)
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Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
Pieter Abeel on Potential Deep Learning Research Directions (2019)
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Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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Five Lessons for Team-Oriented Research with Peter Welder (2019)
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Applied Deep Learning - Rosanne Liu on AI Research (2019)
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Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
Organizing ML projects โ W&B walkthrough (2020)
Weights & Biases
Brandon Rohrer โ Machine Learning in Production for Robots
Weights & Biases
Nicolas Koumchatzky โ Machine Learning in Production for Self-Driving Cars
Weights & Biases
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
VDLS Lavanya Product Walkthrough
Weights & Biases
Testing Machine Learning Models with Eric Schles
Weights & Biases
How Linear Algebra is not like Algebra with Charles Frye
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Predicting Protein Structures using Deep Learning with Jonathan King
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Rachael Tatman โ Conversational AI and Linguistics
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Reformer by Han Lee
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Sequence Models with Pujaa Rajan
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GitHub Actions & Machine Learning Workflows with Hamel Husain
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Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
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Jack Clark โ Building Trustworthy AI Systems
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Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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Antipatterns in open source research code with Jariullah Safi
Weights & Biases
Attention for time series forecasting & COVID predictions - Isaac Godfried
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Made with ML - Goku Mohandas
Weights & Biases
Angela & Danielle โ Designing ML Models for Millions of Consumer Robots
Weights & Biases
DeepCamp AI