In the tensorflow API docs they use a keyword called logits. What is it? A lot of methods are written like:
tf.nn.softmax(logits, name=None)
If logits is just a generic Tensor... moreIn the tensorflow API docs they use a keyword called logits. What is it? A lot of methods are written like:
tf.nn.softmax(logits, name=None)
If logits is just a generic Tensor input, why is it named logits?
Secondly, what is the difference between the following two methods?
tf.nn.softmax(logits, name=None)
tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None)
I know what tf.nn.softmax does, but not the other. An example would be really helpful.
ANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. It's not often clear which method... moreANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. It's not often clear which method is better for a particular project, and I'm certain the answer is always "it depends." Often, a combination of both along with Bayesian classification is used.
These questions on Stackoverflow have already been asked regarding ANN vs SVM:
ANN and SVM classification
what the difference among ANN, SVM and KNN in my classification question
Support Vector Machine or Artificial Neural Network for text processing?
In this question, I'd like to know specifically what aspects of an ANN (specifically, a Multilayer Perceptron) might make it desirable to use over an SVM? The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs:
(1) ANNs often converge on local minima rather than global minima, meaning that they are... less
I am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems... moreI am writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems trivial. Could anyone give me a better idea on how to implement my termination function.
Here in my tree building algorithm.
I'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping... moreI'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?
For example, when mapping the AND function, when I use 2 inputs and 1 output, it does not give the correct weights, however, when I use 3 inputs (1 of which is a bias), it gives the correct weights.
First of all, I'm a beginner studying AI and this is not an opinion-oriented question or one to compare programming languages. I'm not implying that Python is the best language.... moreFirst of all, I'm a beginner studying AI and this is not an opinion-oriented question or one to compare programming languages. I'm not implying that Python is the best language. But the fact is that most of the famous AI frameworks have primary support for Python. They can even be multi language supported, for example, TensorFlow that supports Python, C++, or CNTK from Microsoft that support C# and C++, but the most used is Python (I mean more documentation, examples, bigger community, support, etc). Even if you choose C# (developed by Microsoft and my primary programming language) you must have the Python environment set up.
I read in other forums that Python is preferred for AI because the code is simplified and cleaner, good for fast prototyping.
So what is the big deal with Python?
Why is there a growing association between Python and AI? less
