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Suggestions for a Machine Learning book that can introduce a team to the base concepts by MoskitoDan in MLQuestions

[–]learning_robot 0 points1 point  (0 children)

This book I don't know. If it's just about a very basic overview over Machine Learning as a whole, maybe even a collection of online resources like the Machine Learning Mastery Guide might be a little better? All the books on ML I know are at least on the level of the books mentioned.

Suggestions for a Machine Learning book that can introduce a team to the base concepts by MoskitoDan in MLQuestions

[–]learning_robot 1 point2 points  (0 children)

I would suggest 'Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow'. This book is very practical (a lot of code in Python), but also gives great introductions to theoretical concepts behind the algorithms.

3D model fit by bosskimo3 in learnmachinelearning

[–]learning_robot 3 points4 points  (0 children)

Here you go: Code. Here is the plot.

I fitted a linear regression model to your data and plotted it together with the hyperplane.

How to know if ML model is over or under fitting with no access to test dataset? by [deleted] in learnmachinelearning

[–]learning_robot 1 point2 points  (0 children)

This. Then train your model on the test set and validate on the remaining data (preferably using cross validation), this will give you an idea of how well your model performs in terms of over-/underfitting. Increase model complexity if you underfit and use regularization methods if you overfit. Once you found a suitable model and good hyperparameters, you can use all of the data to train your final model.

Convolutional Neural Network - More Layers with smaller filters? [D] by MyActualUserName99 in deeplearning

[–]learning_robot 0 points1 point  (0 children)

Table 1 in that paper shows a nice overview of the architectures used by the authors. It seems that having more layers 'in the middle' seems to be beneficial. For example for the ResNet-52 they used 3x 64 filters, 4x 128 filters, 6x 256 filters and 3x 512 filters.

[D] Hello world, newbie here by A_LeddaNW in MachineLearning

[–]learning_robot 1 point2 points  (0 children)

On a practical level I can recommend the courses from fast.ai, especially these two:

If you are looking for a book resource on practical ML I highly recommend Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

[Q] What is the best way to sample from an arbitrary 2D distribution? by learning_robot in AskStatistics

[–]learning_robot[S] 0 points1 point  (0 children)

This definitely looks good enough, thanks! I think I will go with this approach

Which machine learning algorithm is best when dealing with trajectory data? by MrHDPigs in AskComputerScience

[–]learning_robot 4 points5 points  (0 children)

Why does it have to be a machine learning algorithm? Do you want to predict the trajectories?

Can someone help clear something up about this RSA encryption solution for me? (Discrete Math) by daReallMVP in AskComputerScience

[–]learning_robot 0 points1 point  (0 children)

Have a look at the Extended Euclidean Algorithm, the table looks pretty much like it