3 ms·

For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometim

by tlow 10y ago

For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work). I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of data from a lot of sensors, including images. I have _no idea_ how I would even get started using Tensor Flow, but it would be cool if I could run experiments on environmental conditions and find optimal conditions for this sweet pea cultivar. Maybe I'm talking nonsense. Let me ask a more basic question, how might one log and create data for use with Tensor Flow. How might Tensor Flow be applied to robotic botanical situations?

minimaxir 10y ago
The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly. Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496 https://news.ycombinator.com/item?id=13464496 However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.
- Houshalter 10y ago
  sklearn has this flowchart for what machine learning method to use: http://scikit-learn.org/stable/_static/ml_map.png http://scikit-learn.org/stable/_static/ml_map.png
  - minimaxir 10y ago
    The flowchart predates NNs/GBTs which are Swiss-army knives, which is another reason why using either of them is sometimes considered cheating.
    - Houshalter 10y ago
      NNs are much older than this chart. They aren't terribly good at problems like this because they tend to overfit more than other methods. They need lots of data to generalize well. They only really excel when the data has regular structure that can be exploited by weight sharing (like CNNs to images or RNNs to time series.)
syntaxing 10y ago
I agree with the previous post that you should focus on Keras rather than Tensorflow. Understanding Tensorflow is a great skill to have because you get a more appreciative and deeper understanding of the models when you dig deeper. But for most application, especially for a fun side project, Keras should be perfect. I recommend http://course.fast.ai/ http://course.fast.ai/ to learn more about the applications of neural networks and how to apply neural networks quickly through python.
- tlow 10y ago
  Thanks.

by tlow 10y ago

minimaxir 10y ago

The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly. Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496 https://news.ycombinator.com/item?id=13464496 However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.

Houshalter 10y ago
sklearn has this flowchart for what machine learning method to use: http://scikit-learn.org/stable/_static/ml_map.png http://scikit-learn.org/stable/_static/ml_map.png
- minimaxir 10y ago
  The flowchart predates NNs/GBTs which are Swiss-army knives, which is another reason why using either of them is sometimes considered cheating.
  - Houshalter 10y ago
    NNs are much older than this chart. They aren't terribly good at problems like this because they tend to overfit more than other methods. They need lots of data to generalize well. They only really excel when the data has regular structure that can be exploited by weight sharing (like CNNs to images or RNNs to time series.)

syntaxing 10y ago

I agree with the previous post that you should focus on Keras rather than Tensorflow. Understanding Tensorflow is a great skill to have because you get a more appreciative and deeper understanding of the models when you dig deeper. But for most application, especially for a fun side project, Keras should be perfect. I recommend http://course.fast.ai/ http://course.fast.ai/ to learn more about the applications of neural networks and how to apply neural networks quickly through python.

tlow 10y ago
Thanks.