David Aponte

Machine learning engineer @benevolent_ai. I love collaborating, learning, and problem-solving.

[Pinterest Engineering @PinterestEng]

Thanks @mlopscommunity for this really great animation based on our Eng Blog article by Shaji Chennan Kunnummel and Iaroslav Tymchenko. #machinelearning #engineering

https://t.co/cPw2T4O3hV

[David Aponte @aponteanalytics]

Checkout a new book by one of the MLOps Community members!

[Alexey Grigorev @Al_Grigor]

8 free copies of Machine Learning Bookcamp!

To get one:
- Follow me
- Retweet this tweet

ML Bookcamp: learn #MachineLearning by doing projects

Results on Wednesday

@ManningBooks
#100DaysOfCode
#100DaysOfMLCode

https://t.co/KfPTZiBhgC

[David Aponte @aponteanalytics]

@DrElleOBrien Lmao yes!

[David Aponte @aponteanalytics]

@hugobowne Hahahaha

[William Falcon ⚡️ @williamfalcon]

Excited to announce the launch of GPT-42.

- Half the size of GPT-3 (100 billion parameters)
- runs on 775 watts a day (2000 calories)
- can do one-shot learning
- multi-modal
- does NOT require 9,000 GPUs
- 300,000 years worth of evolution research!

Inference API coming soon!

[David Aponte @aponteanalytics]

@frontstuff_io These are great! Thank you for sharing :)

[David Aponte @aponteanalytics]

@KirkDBorne Love this book! @KirkDBorne

[David Aponte @aponteanalytics]

Good point.

[Anders Sandberg @anderssandberg]

Many like to say intelligence is not a single thing or a reification of assumptions, IQ is terribly flawed, and the concept has been/is used for bad causes. But so is poverty. So is wealth. So is power. So is inequality. Many important and predictive "things" are like that.

[David Aponte @aponteanalytics]

“It always takes longer than you expect, even when you take into account Hofstadter’s Law” - Hofstadter’s Law

[David Aponte @aponteanalytics]

Working on a repo where you can build pipelines and productionize them using AWS and GCP. Use it to start your own project! #MachineLearning #DataScience #softwareengineering https://t.co/0Q9JwfXgTR

[David Aponte @aponteanalytics]

@abhi1thakur Just watched your first video and I really liked it! Quick question about how you setup the project: is there an advantage to running the https://t.co/XhTnwF28hv file using ‘python -m src.train’ over ‘python src/train.py’? Thanks!

[David Aponte @aponteanalytics]

@abhi1thakur What type of setup do you have?

[François Chollet @fchollet]

A while ago, I thought doing things in a clever way was cool. Now, I understand that simple is better than clever. Clever is mostly showing off.

[François Chollet @fchollet]

So, "deep learning" is the idea of doing representation learning via a chain of learned feature extractors. It's all about describing some input data via *deep hierarchies of features*, where features are *learned*.

A further question is then: is the brain "deep learning"?

[Chip Huyen @chipro]

Looking back, my last decade was like a neural network. Some parts were linear. Some were nonlinear. I never seemed to get enough data, and always got stuck in local minima. There was a lot of learning. I can't explain how any of it worked, but the results came out alright.

[David Aponte @aponteanalytics]

@justmarkham @TedPetrou These tips are great, thank you!

[PyLatam @PyLatam]

ADN #PyConLatam! 💙

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[François Chollet @fchollet]

🌊 = 🚰()
🟫 = 🚜()
with tf.GardenTape() as 📼:
x = 👨‍🌾(🟫, 🌊)
x = 🌅(x)
x = 👩‍🌾(x)
[🌷, 🌼, 🌻] = 📼.garden(x, [🌊, 🟫])

[Teddy Petrou @TedPetrou]

The scikit-learn TransformedTargetRegressor can make a fantastic addition to your workflow. Instead of transforming your target before fitting and then after prediction, it automates the process for you.

Here's an example from the Kaggle Housing competition