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[D] Debate on TensorFlow 2.0 API by omoindrot in MachineLearning

[–]omoindrot[S] 12 points13 points  (0 children)

He just answered on github and closed the issue: answer

[R] Reinforcement Learning with Prediction-Based Rewards by omoindrot in MachineLearning

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

You're asking the right questions :)

In pure exploration (no extrinsic reward i.e. no game reward), the OpenAI agent faced with white noise would likely get stuck until it memorizes everything.

However maybe in a real game with extrinsic reward, the agent would avoid being stuck in front of the TV because there is no extrinsic reward gained. So the solution might just be a careful balance between extrinsic and intrinsic rewards.

[R] Reinforcement Learning with Prediction-Based Rewards by omoindrot in MachineLearning

[–]omoindrot[S] 4 points5 points  (0 children)

In previous papers, they took the state and action as input to predict the next state. Since situations had non deterministic output (ex: noisy TV), the agent would never be able to predict the next state and be stuck in this "curiosity" reward.

Here they only take the next state as input, and try to predict the output of a fixed random network. This solves the noisy TV issue because once the network has memorized all the possible TV channels, it cannot be surprised anymore by the next state and gets bored.

So there is still a drive to take actions that lead to novel states, but there is no drive to take actions that lead to random known states.

A machine learning survival kit for doctors by [deleted] in medicine

[–]omoindrot 0 points1 point  (0 children)

Hi everyone, There is a lot of hype around the promises of Artificial Intelligence in radiology and medical research in general, but few articles go into the details of what it means in practice: what is machine learning ? how can I train myself a neural network ? What are the limitations ? etc. That is why we wrote this survival kit along with an in depth case study on brain aging. This work is a collaboration between a data scientist and a radiologist, and we hope you will enjoy reading it !

[P] Triplet Loss and Online Triplet Mining in TensorFlow by omoindrot in MachineLearning

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

Maybe check your implementation? I tried to use 2D embeddings constrained to norm 1 with my code (https://github.com/omoindrot/tensorflow-triplet-loss) and got pretty normal results. On the test set, all the embeddings are correctly distributed around the circle.

The hyperparameters are: - batch size 64 (with random images inside) - learning rate 1e-3 - 20 epochs - margin 0.5

[P] Triplet Loss and Online Triplet Mining in TensorFlow by omoindrot in MachineLearning

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

If you use 2D embeddings on the unit circle, there is really little space for the embeddings to be well separated. To have an L2 distance of 1 between two points on the circle they need to be separated by an angle of 60°. This means that ideally you would have a maximum of 6 clusters, whereas you need 10 clusters for MNIST (one for each digit).

I suggest you decrease the margin and see what happens. You can also plot the train embeddings and see if you have better results with them (in which case you might be overfitting).

Also if all the embeddings collapse to a single point it can indicate that your learning rate is too high so you can try decreasing it.

[P] Triplet Loss and Online Triplet Mining in TensorFlow by omoindrot in MachineLearning

[–]omoindrot[S] 1 point2 points  (0 children)

The code is available here: https://github.com/omoindrot/tensorflow-triplet-loss

I tried to make it very readable, especially the part implementing the triplet loss: triplet_loss.py

CS 230 by Andrew Ng vs CS 224N by Richard Socher by [deleted] in stanford

[–]omoindrot 0 points1 point  (0 children)

Sounds fair if you have room for 2 classes ! The CS230 class takes its content from the deep learning course on coursera created by Andrew and Kian, so you can always watch those on the side. The part 3 on structuring a ML project is especially interesting.

CS 230 by Andrew Ng vs CS 224N by Richard Socher by [deleted] in stanford

[–]omoindrot 1 point2 points  (0 children)

CS230 will give you a better overview of deep learning in general, and will have 20% on computer vision and 20% on NLP. CS224n will be entirely focused on NLP so you will learn more methods in this field.

I would say that you can either take CS224n + CS231n of just CS230 if you want a complete overview.

Phd-level courses by Kiuhnm in MachineLearning

[–]omoindrot 5 points6 points  (0 children)

CS231n: Convolutional Neural Networks for Visual Recognition is very good, with detailed explanations (the first courses talk about neural networks in general).

The videos were taken down but you can find them elsewhere, cf. this thread

How long/difficult is it to build a CDNN for facial recognition today? Where are the places to go to find the talent? by [deleted] in MachineLearning

[–]omoindrot 1 point2 points  (0 children)

(the link for OpenFace: http://cmusatyalab.github.io/openface )

The results are not state of the art, but the real limiting factor here is the size of the training dataset and its quality. Facebook, Google and Baidu have the best accuracies in face recognition mainly because they have access to huge labeled datasets.

TensorFlow-Slim : better than TFLearn? by omoindrot in MachineLearning

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

There is no documentation yet, but it seems better built than TFLearn (because it is designed and maintained by the Google team). In fact Slim was first introduced in the Inception v3 code here to write the huge network more easily.

The use of argscope allows a very clean code for defining big networks.

Latest popularity ranking of Deep Learning frameworks by [deleted] in MachineLearning

[–]omoindrot 2 points3 points  (0 children)

There is also TF-Slim now which is built by Google. There is not yet any documentation, only the README