Data scientist molding data into smarter artifacts. Author on AI, machine learning, and algorithms for Wiley, Packt, Manning. 3x Kaggle Grandmaster. GDE in AI.
Joined August 2017
- Tweets671
- Following270
- Followers1.4K
- Likes2.3K
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
Two weeks ago, I concluded over four years driving influencer & creator initiatives at @NVIDIA.
One thing I'm most grateful for is the community. The creators and developers that showed up with real curiosity, thoughtful questions, and candor always helped push myself and the team further. I've loved watching the different ways creators dig into AI and the NVIDIA stack while finding a way to explain in a way that lands with their audience.
To everyone I got to work with - creators, developers, researchers, NVIDIANs, agencies, partners, the lot - thank you for your trust, wisdom, and time. I'm leaving with relationships I'm proud of, and I'll be right there cheering as you keep building.
Special shoutout to my former teammates (tagging some here: @michaelcooper1, @jjang236, @aakap22, @marcpion, @jonah_chin) - it’s a privilege to work with them since they’re awesome at what they do.
If you're working on something cool or want to chat, feel free to send me a message!
Luca Massaron retweeted
“The Kaggle Workbook: Self-Learning Exercises and Valuable Insights for @Kaggle Data Science Competitions": amzn.to/41jKa7r by @tng_konrad and @LucaMassaron from @PacktPublishing
Luca Massaron retweeted
Yes! Here it is: xitter.cf/NVIDIAAI/status/211074…
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
As a Google Developer Expert (GDE) or when working on personal projects, I constantly evaluate new open-weight language models to decide whether they belong in my stack.
In this article, I will break down exactly how I test a new model like Gemma 4 12B: medium.com/@lucamassaron/gem…
🤖 Made with AI
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
I’m very excited to share @nvidia Kumo Tabular, a new family of foundation models for tabular data.
Kumo Tabular establishes the new Pareto frontier across the entire accuracy–inference-time tradeoff.
Just as importantly, we are releasing it openly: open weights, open-source software, and a permissive license for commercial use.
HuggingFace: huggingface.co/nvidia/Kumo-T…
GitHub: github.com/NVIDIA/structured…
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
See all of the books by @lucamassaron and his co-authors — for your favorite Data Scientist (that's you!): amzn.to/4pKoFYe
📊📈🚀✨🏆
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
Machine learning doesn't end when the model is trained.
You still need to evaluate it, deploy it, and build the infrastructure to keep it working in production.
Today's Deal of the Day takes you across the ML lifecycle with four books:
• Deep Learning with Python, Third Edition by @fchollet and Matthew Watson
• Machine Learning for Tabular Data by @MarkRyanMkm and @lucamassaron
• AI Model Evaluation by @LeemayNassery
• Machine Learning Platform Engineering by @bentanweihao, Shanoop Padmanabhan, and Varun Mallya
All four are 50% off, plus more: hubs.la/Q04w_2CT0
Luca Massaron retweeted
🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: amzn.to/4pxJpTC v/ @PacktDataML
Table of Contents:
🔶Introducing Data Science Competition
🔷Organizing Data with Datasets
🔶Work & Learn with Kaggle Notebooks
🔷Kaggle Models
🔶Leveraging Discussion Forums
🔷Detailing Competition Tasks & Metrics
🔶Designing Good Validation Schemes
🔷Modeling for Tabular Competitions
🔶Hyperparameter Optimization
🔷Ensembling & Stacking Solutions
🔶Modeling Image Classification & Segmentation
My Review (on Amazon):
This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: amzn.to/4pxJpTC v/ @PacktDataML
Table of Contents:
🔶Introducing Data Science Competition
🔷Organizing Data with Datasets
🔶Work & Learn with Kaggle Notebooks
🔷Kaggle Models
🔶Leveraging Discussion Forums
🔷Detailing Competition Tasks & Metrics
🔶Designing Good Validation Schemes
🔷Modeling for Tabular Competitions
🔶Hyperparameter Optimization
🔷Ensembling & Stacking Solutions
🔶Modeling Image Classification & Segmentation
My Review (on Amazon):
This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.
Luca Massaron retweeted
“The Kaggle Workbook: Self-Learning Exercises and Valuable Insights for @Kaggle Data Science Competitions": amzn.to/41jKa7r by @tng_konrad and @LucaMassaron
——————
#Analytics #DataScientist #AI #DeepLearning #ML #MachineLearning #Python
Luca Massaron retweeted
❤️ Give Tabular Data some love! ❤️
Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron
Get it here: amzn.to/41J8WA6
• Master XGBoost
• Apply deep learning to tabular data
• Deploy models locally and in the cloud
• Build pipelines to train and maintain models
Luca Massaron retweeted
Using Antigravity to automate and accelerate PM tasks: medium.com/google-cloud/anti…
Luca Massaron retweeted
See all the books by @lucamassaron and his co-authors — for your favorite Data Scientist (that's you!): amzn.to/4pKoFYe
📊📈🚀✨🏆
#DataScience #AI #DeepLearning #ML #Algorithms #MachineLearning #Python
Luca Massaron retweeted
🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: amzn.to/4pxJpTC v/ @PacktDataML
Table of Contents:
🔶Introducing Data Science Competition
🔷Organizing Data with Datasets
🔶Work & Learn with Kaggle Notebooks
🔷Kaggle Models
🔶Leveraging Discussion Forums
🔷Detailing Competition Tasks & Metrics
🔶Designing Good Validation Schemes
🔷Modeling for Tabular Competitions
🔶Hyperparameter Optimization
🔷Ensembling & Stacking Solutions
🔶Modeling Image Classification & Segmentation
My Review (on Amazon):
This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.
