What is representation learning? - Data Science Stack Exchange
2017年8月17日 · The authors cite the autoencoder as an example for a representation learning algorithm. An autoencoder is the combination of an encoder function, which converts the input data into a different representation, and a decoder function, which converts the new representation back into the original format
deep learning - What are latent representations? - Data Science …
2021年2月28日 · The latent representation is the simplified model of your input data, for example, created by a neural network. Considering an autoencoder, the central layer of this network (after training) will contain a simplified representation of the input data (i.e. summary of key features), which can be used to reconstruct the output.
What is the difference between "equivariant to translation" and ...
Regarding an image classification task solved with a typical CNN Architecture consisting of a Backend (Convolutions + NL + possibly Spatial Pooling) which performs Representation Learning and of a Frontend (e.g. Fully Connected Layers, MLP) which solves the specific task, in this case image classification, the idea is to build a function $ f ...
neural network - What does embedding mean in machine …
2019年6月18日 · In the context of machine learning, an embedding is a low-dimensional, learned continuous vector representation of discrete variables into which you can translate high-dimensional vectors. Generally, embeddings make ML models more efficient and easier to work with, and can be used with other models as well.
machine learning - How do you visualize neural network …
$\begingroup$ I wrote Simple diagrams of convoluted neural networks with a survey of deep learning visualization approaches (both manual and automatic). I got a lot of inspiration, and links, from this thread - thx! $\endgroup$
Representing state in Q-Learning - Data Science Stack Exchange
2019年5月4日 · This kind of approximation is also the main driver behind "Deep" Q-learning (DQN), which is essentially Q-learning with a neural network approximating the action value table. There are some details to add to get this to work well - but plenty of resources available to help with this on the internet: Tutorials, examples and fully coded agents ...
How to draw Deep learning network architecture diagrams?
2016年11月3日 · We can use Powerpoint to get the job done. Draw the diagram (3D rectangles and perspectives come handy) -> select the interested area on the slide -> right-click -> Save as picture -> change filetype to PDF -> :)
What does Logits in machine learning mean?
2018年4月30日 · Also, note that in some deep learning frameworks, when computing loss functions associated to probabilities, normally the logits are used as input instead of the normalized probabilities, e.g. cross-entropy loss in PyTorch: The input is expected to contain raw, unnormalized scores for each class
python - Is it possible to extract mathematical expression of an ...
2022年11月11日 · In Python & R, Linear Regression model gives the mathematical representation after learning the training data, typically in the form of intercept, coefficients of variables, and the p-value/t-value. Is it possible to extract mathematical representation of complex models based on Decision Tree, Random Forest, XGBoost, etc. after training it on a ...
machine learning - Sparse Representation vs Dense …
2018年11月22日 · Here dense representations will have lesser degrees of freedom, resulting in non-linear relationship. Overall our representation mathematically needs to mimic the topology of underlying manifold. But in how many scenarios can we have sparse representation, is the bigger question/problem, where we we have sufficient uncorrupted identities.