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[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
07/03/20 • 36 min
In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose new ones.
This episode is brought to you by Protonmail
Click on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.
In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose new ones.
This episode is brought to you by Protonmail
Click on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.
Previous Episode
Rust and machine learning #4: practical tools (Ep. 110)
In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly.
To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).
Rust is the language of the future.
Happy coding!
- BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
- Rust dataframe https://github.com/nevi-me/rust-dataframe
- Rustlearn https://github.com/maciejkula/rustlearn
- Rusty machine https://github.com/AtheMathmo/rusty-machine
- Tensorflow bindings https://lib.rs/crates/tensorflow
- Juice (machine learning for hackers) https://lib.rs/crates/juice
- Rust reinforcement learning https://lib.rs/crates/rsrl
Next Episode
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
In this episode I speak about data transformation frameworks available for the data scientist who writes Python code.
The usual suspect is clearly Pandas, as the most widely used library and de-facto standard. However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected. Other frameworks play a role in such context.
In this episode I explain the frameworks that are the best equivalent to Pandas in bigdata contexts.
Don't forget to join our Discord channel and comment previous episodes or propose new ones.
This episode is supported by Amethix Technologies
Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve. Amethix is a consulting firm focused on data science, machine learning, and artificial intelligence.
References- Pandas a fast, powerful, flexible and easy to use open source data analysis and manipulation tool - https://pandas.pydata.org/
- Modin - Scale your pandas workflows by changing one line of code - https://github.com/modin-project/modin
- Dask advanced parallelism for analytics https://dask.org/
- Ray is a fast and simple framework for building and running distributed applications https://github.com/ray-project/ray
- RAPIDS - GPU data science https://rapids.ai/
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