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A tartalmat a SmartLogic LLC biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a SmartLogic LLC vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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Machine Learning in Elixir vs. Python, SQL, and Matlab with Katelynn Burns & Alexis Carpenter

31:19
 
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Manage episode 385342062 series 2493466
A tartalmat a SmartLogic LLC biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a SmartLogic LLC vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs.

The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively.

Topics discussed:

  • Using pre-trained models in Bumblebee for Elixir projects
  • Training models using Python and SQL
  • The importance of data preprocessing before building models
  • Popular tools used for machine learning in different languages
  • Getting started with ML by picking a personal project topic of interest
  • Resources for ML aspirants, such as online courses, tutorials, and books
  • The potential for Elixir to train more customized models in the future
  • Similarities between ML approaches in different languages
  • Collaboration opportunities across programming communities
  • Choosing the right ML approach for the problem you're trying to solve
  • Productionalizing models like fine-tuned LLM's
  • The need for hands-on practice for learning ML skills
  • Continued maturation of tools like Bumblebee in Elixir
  • Katelynn's upcoming CodeBeam talk on advanced motion tracking

Links mentioned in this episode

https://launchscout.com/
https://www.cars.com/
Genetic Algorithms in Elixir by Sean Moriarity
Machine Learning in Elixir by Sean Moriarity
https://github.com/elixir-nx/bumblebee
https://github.com/huggingface
https://www.docker.com/products/docker-hub/
Programming with MATLAB
https://elixirforum.com/
https://pypi.org/project/pyspark/
Machine Learning Course from Stanford School of Engineering
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Data Science for Business by Foster Provost & Tom Fawcett
https://medium.com/@carscomtech
https://github.com/k-burns
Code Beam America March, 2024

Special Guests: Alexis Carpenter and Katelynn Burns.

  continue reading

182 epizódok

Artwork
iconMegosztás
 
Manage episode 385342062 series 2493466
A tartalmat a SmartLogic LLC biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a SmartLogic LLC vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs.

The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively.

Topics discussed:

  • Using pre-trained models in Bumblebee for Elixir projects
  • Training models using Python and SQL
  • The importance of data preprocessing before building models
  • Popular tools used for machine learning in different languages
  • Getting started with ML by picking a personal project topic of interest
  • Resources for ML aspirants, such as online courses, tutorials, and books
  • The potential for Elixir to train more customized models in the future
  • Similarities between ML approaches in different languages
  • Collaboration opportunities across programming communities
  • Choosing the right ML approach for the problem you're trying to solve
  • Productionalizing models like fine-tuned LLM's
  • The need for hands-on practice for learning ML skills
  • Continued maturation of tools like Bumblebee in Elixir
  • Katelynn's upcoming CodeBeam talk on advanced motion tracking

Links mentioned in this episode

https://launchscout.com/
https://www.cars.com/
Genetic Algorithms in Elixir by Sean Moriarity
Machine Learning in Elixir by Sean Moriarity
https://github.com/elixir-nx/bumblebee
https://github.com/huggingface
https://www.docker.com/products/docker-hub/
Programming with MATLAB
https://elixirforum.com/
https://pypi.org/project/pyspark/
Machine Learning Course from Stanford School of Engineering
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Data Science for Business by Foster Provost & Tom Fawcett
https://medium.com/@carscomtech
https://github.com/k-burns
Code Beam America March, 2024

Special Guests: Alexis Carpenter and Katelynn Burns.

  continue reading

182 epizódok

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