Attendees should have experience coding in Python and familiarity with matrix algebra. Having a computer that can connect to the internet is a necessity. No downloads or software installations are required, but it is recommended that attendees have either Chrome or Firefox installed on their computers.
The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Dana Mastropole details TensorFlow’s capabilities through its Python interface, moving from building machine learning algorithms piece by piece to using the higher-level abstractions provided by TensorFlow. You’ll use this knowledge to build machine learning models on real-world data.
You’ll be provisioned a cloud instance with TensorFlow as a part of this course.
Day 1
Day 2
Dana Mastropole is a data scientist in residence at the Data Incubator and contributes to curriculum development and instruction. Previously, Dana taught elementary school science after completing MIT’s Kaufman teaching certificate program. She studied physics as an undergraduate student at Georgetown University and holds a master’s in physical oceanography from MIT.
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Comments
Hello,
We will train machine learning models using TensorFlow’s Python API, so having some experience coding in Python is required. In order to understand the implementation and theory behind these models, a basic understanding of matrix algebra, calculus, and statistics is also helpful. For example, it is assumed that attendees know how to multiply two matrices, how to take a derivative, and what a probability distribution is. The models we will cover include linear regression, logistic regression, and neural networks.
Best,
Dana
Hello,
what kind of “Familiarity with Python, matrices, modeling, and statistics” is required to take this training? Especially is it required to have knowledge about topics that will be discussed (e.g. convolutional neural networks)?
Best regards
Hi Daniele,
I’ll answer your questions in order:
- The training will cover the same material (with a few updates/modifications) as last year’s TensorFlow workshop at London’s Strata Data conference.
- The exercises will be done on CPUs only.
- We will be using jupyter notebooks as the teaching platform.
Dear Dana,
a couple of questions if I may:
- in what (if any) is this training different in content or more/less advanced than the same one from DataIncubator that was held at last year’s Strata Data conference in London?
- will TF-based training be done on GPUs in the exercises, or only on CPUs?
- will you be using python jupiter notebooks as the main teaching platform, or what else?
Thanks!
Hi Lise, the prerequisites are the following:
- Python
- Familiarity with matrices
- Familiarity with modeling
- Familiarity with statistics
Hi Dana
What are the prerequisites for the training?
Thanks
Yes, the training will be on your personal computer. We recommend that you have either chrome or firefox.
Hello, is the training to be made with personal laptops ? if it is, is there a suggested preconfiguration ?