Suzana Iliฤ โ Cultivating Machine Learning Communities
๐ฉโ๐ปToday our guest is Suzana Iliฤ!
Suzana is a founder of Machine Learning Tokyo which is a nonprofit organization dedicated to democratizing Machine Learning. They are a team of ML Engineers and Researchers and a community of more than 3000 people.
Follow Suzana on twitter: https://twitter.com/suzatweet
Topics covered:
0:00 Sneek peek
0:19 Suzana Bio
1:00 Why did you start Machine Learning Tokyo?
5:45 What did you do at your first gatherings?
7:42 how do you run a good workshop?
11:02 Differences between SF/Tokyo
13:37 WandB Ad
15:14 On the democratization of AI
18:48 Advice for people waโฆ
Watch on YouTube โ
(saves to browser)
Chapters (13)
Sneek peek
0:19
Suzana Bio
1:00
Why did you start Machine Learning Tokyo?
5:45
What did you do at your first gatherings?
7:42
how do you run a good workshop?
11:02
Differences between SF/Tokyo
13:37
WandB Ad
15:14
On the democratization of AI
18:48
Advice for people wanting to go deep into machine learning
22:40
How to find good ML groups where you are (or build one yourself!)
25:40
Diversity in ML
29:50
Underrated aspect of machine learning
31:25
Hardest part of making ML work in the real world
Playlist
Uploads from Weights & Biases ยท Weights & Biases ยท 0 of 60
โ Previous
Next โ
1
2
3
4
5
6
7
8
9
10
![6. Recurrent Neural Networks [RNNs]](https://i.ytimg.com/vi/8lbGjKhrJOo/mqdefault.jpg)
11
12
13
![9. Hybrid LSTMs [Long Short-Term Memory]](https://i.ytimg.com/vi/NysY9FN9Uac/mqdefault.jpg)
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
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)
Weights & Biases
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
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
Weights & Biases
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
Rachael Tatman โ Conversational AI and Linguistics
Weights & Biases
Reformer by Han Lee
Weights & Biases
Sequence Models with Pujaa Rajan
Weights & Biases
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
Weights & Biases
Jack Clark โ Building Trustworthy AI Systems
Weights & Biases
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Weights & Biases
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
Made with ML - Goku Mohandas
Weights & Biases
Angela & Danielle โ Designing ML Models for Millions of Consumer Robots
Weights & Biases
DeepCamp AI