Daniel Bourke
Every month I publish a newsletter called Machine Learning Monthly, which gathers the latest, greatest and most important things happening in the field of machine learning.
And hoho… this issue was an absolute banger! What a phenomenal month on tour for the world of machine learning. Be sure to subscribe/submit your work for next month’s letter.
ML Monthly November 2020 – https://zerotomastery.io/blog/machine-learning-monthly-november-2020/
Learn ML (my beginner-friendly course) – https://dbourke.link/ZTMMLcourse
Links mentioned:
My upcoming deep learning with TensorFlow course – https://github.com/mrdbourke/tensorflow-deep-learning
Alvaro’s guide to TorchServe – https://github.com/alvarobartt/serving-pytorch-models
TensorFlow for Mac – https://blog.tensorflow.org/2020/11/accelerating-tensorflow-performance-on-mac.html
TensorFlow Recommenders (TFRS) – https://blog.tensorflow.org/2020/09/introducing-tensorflow-recommenders.html
NYU Deep Learning course – https://cds.nyu.edu/deep-learning/
ML created art – https://mlart.co/
Deep Learning’s Most Important Ideas blog post – https://dennybritz.com/blog/deep-learning-most-important-ideas/
Continuous delivery for ML – https://martinfowler.com/articles/cd4ml.html
Why we need DevOps for ML data – https://www.tecton.ai/blog/devops-ml-data/
Data Valuation using Reinforcement Learning (DVRL) – https://proceedings.icml.cc/static/paper_files/icml/2020/3003-Paper.pdf
Estimating Example Difficulty Using Variance of Gradients (VOG) – https://arxiv.org/abs/2008.11600
AI camerman mistakes bald referee’s head for ball – https://www.iflscience.com/technology/ai-camera-ruins-soccar-game-for-fans-after-mistaking-referees-bald-head-for-ball/
Timestamps:
0:00 – Intro/hello
1:40 – My upcoming TensorFlow course
3:53 – Alvaro’s guide to TorchServe
7:10 – TensorFlow for Mac
11:25 – TensorFlow Recommenders
13:37 – Yann LeCun’s NYU Deep Learning course
16:15 – Machine learning created art
18:05 – Robot draws poetry on the beach
19:00 – Deep learning’s most import ideas by Denny Britz
25:55 – Continuous delivery for machine learning
29:40 – Why we need DevOps for ML data
33:51 – Papers: How hard is your data to model?
34:20 – 📄 Data Valuation Using Reinforcement Learning
36:33 – 📄 Estimating Example Difficulty Using Variance of Gradients
40:00 – AI cameraman mistakes bald referee’s head for soccer ball
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