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 don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better.
Do I use the "acc" (from the training data?) one or the "val acc"... moreI don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better.
Do I use the "acc" (from the training data?) one or the "val acc" (from the validation data?) one?
There are different accs and val accs for each epoch. How do I know the acc or val acc for my model as a whole? Do I average all of the epochs accs or val accs to find the acc or val acc of the model as a whole?Model 1 Output
Train on 970 samples, validate on 243 samples
Epoch 1/20
0s - loss: 0.1708 - acc: 0.7990 - val_loss: 0.2143 - val_acc: 0.7325
Epoch 2/20
0s - loss: 0.1633 - acc: 0.8021 - val_loss: 0.2295 - val_acc: 0.7325
Epoch 3/20
0s - loss: 0.1657 - acc: 0.7938 - val_loss: 0.2243 - val_acc: 0.7737
Epoch 4/20
0s - loss: 0.1847 - acc: 0.7969 - val_loss: 0.2253 - val_acc: 0.7490
Epoch 5/20
0s - loss: 0.1771 - acc: 0.8062 - val_loss: 0.2402 - val_acc: 0.7407
Epoch 6/20
0s - loss: 0.1789 - acc: 0.8021 - val_loss: 0.2431 - val_acc: 0.7407
Epoch 7/20
0s - loss: 0.1789 - acc: 0.8031 -... less
I am training on 970 samples and validating on 243 samples.
How big should batch size and number of epochs be when fitting a model in Keras to optimize the val_acc? Is there any... moreI am training on 970 samples and validating on 243 samples.
How big should batch size and number of epochs be when fitting a model in Keras to optimize the val_acc? Is there any sort of rule of thumb to use based on data input size?
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