Contents
How to choose the best hyperparameters for LSTM?
Since there are many great courses on the math and general concepts behind Recurring Neural Networks (RNN), e.g. Andrew Ng’s deep learning specialization or here on Medium, I will not dig deeper into them and perceive this knowledge as given. Instead, we will only focus on the high-level implementation using Keras.
How are Siamese networks used for object tracking?
Recently, Siamese networks have drawn great attention in the context of short-term tracking because of their balanced accuracy and speed. Siamese trackers [6], [11] formulate object tracking as a one-shot learning task and can be trained offline to learn a generic similarity function from a large set of videos.
How is hard negative mining used in siamese networks?
(a) A hard negative mining method is proposed. It can optimize the training process of the verification network and bridge the gap between the two sub-networks. (b) The architecture of the verification network is designed as a Siamese structure; therefore, the semantic ambiguity in classification can be alleviated.
What are the features of hyperparameter tuning in deep learning?
Features like hyperparameter tuning, regularization, batch normalization, etc. come to the fore during this process. This is part 2 of the deeplearning.ai course (deep learning specialization) taught by the great Andrew Ng.
Which is the one hot encoding in LSTM?
The method we’ll be using is the so-called One-Hot Encoding. Here, every word is represented by a vector of n binary sub-vectors, where n is the number of different chars in the alphabet (26 using the English alphabet).
How to convert LSTM predictions to inverse transform?
Upon generating the predictions from LSTM, the same can be converted back using scaler.inverse_transform as follows: Thanks for contributing an answer to Data Science Stack Exchange!
When to add a second layer to a LSTM?
As a general rule of thumb — 1 hidden layer work with simple problems, like this, and two are enough to find reasonably complex features. In our case, adding a second layer only improves the accuracy by ~0.2% (0.9807 vs. 0.9819) after 10 epochs. Every LSTM layer should be accompanied by a Dropout layer.