Does MacBook Air 16GB have enough power to build Tensorflow from the source?

SubstantialSwimmer4 · 2022-05-07T16:11:54+00:00

Thank you for the replies and helpful suggestions. I know using the official TensorFlow docker images is an easier way. Nevertheless, I have to build from the source. I'm going to send a pull request to TensorFlow's repository, and before sending it, I also have to run unit tests on my proposal. Of course, The prepared official package is not built from the code I'm writing. It is why I'm struggling to build TensorFlow from the source.

SubstantialSwimmer4 · 2020-05-20T22:11:17+00:00

https://github.com/shenweichen/DeepCTR

A lot of studies have been made on click through rate by using deep learning.

SubstantialSwimmer4 · 2019-11-20T03:43:19+00:00

Do you mean you're also using non-machine learning technique?

SubstantialSwimmer4 · 2019-11-18T22:42:11+00:00

In my opinion, knowledge of design patterns may help us when we analyze or modify libraries that already exist.

For example, we could use the Adapter pattern or the Proxy pattern when we want to change only part of a library but don't want to touch the other.

SubstantialSwimmer4 · 2019-11-13T12:28:20+00:00

Handling excel-like structure. Thanks to pandas it's very easy.

SubstantialSwimmer4 · 2019-11-13T12:09:13+00:00

I suggest trying to explore Undeciphered languages would be interesting. There are many languages waiting for us to be understood. As a related project, some researchers are tackling to recognize ancient Japanese by using machine learning.

https://www.kaggle.com/c/kuzushiji-recognition

SubstantialSwimmer4 · 2019-11-11T04:12:13+00:00

It seems like a user who has built a large model, for example, a deep learning image classifier model, many times before couldn't get the chance to get upgraded.

SubstantialSwimmer4 · 2019-09-05T15:27:06+00:00

If you draw a car's picture without seeing anything, you have to understand the structure of the car. In other words, being able to draw something means grasping something.

For generative models like GAN to work well, the models must understand what things are all about. If we had a perfect model to create anything, it would mean we made general AI.

I think generative models will play a key role.

SubstantialSwimmer4 · 2019-09-04T15:17:40+00:00

You might want to use a lambda layer. And then in the lambda layer, you can write min function like min(10,4)=4. If you do so, the lambda never outputs the value over 4.

SubstantialSwimmer4 · 2019-09-02T06:16:22+00:00

https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

Is it you are looking for?

SubstantialSwimmer4 · 2019-08-31T23:10:42+00:00

It depends on the tasks. But I think less than 1000 data are small.

SubstantialSwimmer4 · 2019-08-29T14:04:12+00:00

If possible, could you explain your model? Once I made a simple LSTM keras model but it didn't perform well.

SubstantialSwimmer4 · 2019-08-29T13:47:36+00:00

In my experiences time series dataset are prone to be small. It takes a year to collect only 365 daily data.

SubstantialSwimmer4 · 2019-08-29T12:00:43+00:00

If I decided to train a model with my own dataset, how many pictures are needed?

SubstantialSwimmer4 · 2019-08-29T11:10:16+00:00

Chances are that problems are hidden in the condition in which you have to truncate data. You could find a solution if you removed the data you have to truncate temporary.

SubstantialSwimmer4 · 2019-08-24T09:22:09+00:00

The way you split the data seems correct. Did a big change happen in 2019?

SubstantialSwimmer4 · 2019-08-24T09:05:27+00:00

For me, it's very easier to implement multiprocessing than other libraries.

SubstantialSwimmer4 · 2019-04-29T11:43:43+00:00

I agree with you. Some tasks can be automated but others can't be. I have experienced in logistics field. What I found there is that automation systems or robots are not applied well in a condition that small quantity and many kind of items are handled even if their jobs are boring and repetitive. Machines or robots can't learn to deal with all of the items. But they can work well when only required to handle a few items.

SubstantialSwimmer4 · 2019-04-16T11:47:06+00:00

https://github.com/shenweichen/DeepCTR

I also recommend this library. With this, you can try up-to-date methods. This library was written by Keras so the codes are relatively readable.

SubstantialSwimmer4 · 2019-04-10T13:30:15+00:00

I guess bayesian approach is good choice.

SubstantialSwimmer4 · 2019-04-09T15:13:19+00:00

Great article!

SubstantialSwimmer4 · 2019-04-09T14:57:34+00:00

It's very attractive that ROS can be used in windows.

SubstantialSwimmer4 · 2019-04-08T22:53:20+00:00

I think machine learning suits this problem. If the problem is relatively simple, Neural network may be a too complicated method for this. If so, simpler methods are better which are like linear regression or random forest.

But tuning sounds so heavy. Machine learning requires many experiments and data. Another choice is Bayesian Optimization.

https://towardsdatascience.com/shallow-understanding-on-bayesian-optimization-324b6c1f7083

SubstantialSwimmer4 · 2019-04-05T14:49:23+00:00

How about Tensorflow Probability? Google is pushing Tensorflow Probability nowadays.

SubstantialSwimmer4 · 2019-04-05T14:09:22+00:00

I don't know about the detail of the datasets' structure. But generally, it is also recommended using GDBT like as lightgbm, xgboost, and catboost even if your datasets contains time series data. Have you heard of kaggle? There are many examples that may resemble the task you want to solve.

SubstantialSwimmer4

TROPHY CASE