How do I get high accuracy on CNN?

Train with more data: Train with more data helps to increase accuracy of mode. Large training data may avoid the overfitting problem. In CNN we can use data augmentation to increase the size of training set….

1. Tune Parameters.
2. Image Data Augmentation.
3. Deeper Network Topology.
4. Handel Overfitting and Underfitting problem.

How do you increase the accuracy of a deep learning model?

Here is the checklist to improve performance:

1. Analyze errors (bad predictions) in the validation dataset.
2. Monitor the activations.
3. Monitor the percentage of dead nodes.
4. Apply gradient clipping (in particular NLP) to control exploding gradients.
5. Shuffle dataset (manually or programmatically).

How can you increase the accuracy of a small data set?

We’ll now discuss the seven most useful techniques to avoid overfitting when working with small datasets.

1. Choose simple models.
2. Remove outliers from data.
3. Select relevant features.
4. Combine several models.
5. Rely on confidence intervals instead of point estimates.
6. Extend the dataset.
7. Apply transfer learning when possible.

Can deep learning work on small data?

A recent paper , Deep Learning on Small Datasets without Pre-Training using Cosine Loss, found a 30\% increase in accuracy for small datasets when switching the loss function from categorical cross-entropy loss to a cosine loss for classification problems. Cosine loss is simply 1 — cosine similarity .

Why is my training accuracy so low?

If the training accuracy is low, it means that you are doing underfitting (high bias). Some things that you might try (maybe in order): Increase the model capacity. Add more layers, add more neurons, play with better architectures.

What is the difference between RNN and CNN in deep learning?

This is mostly because RNN has gradient vanishing and exploding problems (over 3 layers, the performance may drop) whereas CNN can be stacked into a very deep model, for which it’s been proven quite effective. But CNNs are not also flawless.

What are the different types of neural networks in deep learning?

This article focuses on three important types of neural networks that form the basis for most pre-trained models in deep learning: 1 Artificial Neural Networks (ANN) 2 Convolution Neural Networks (CNN) 3 Recurrent Neural Networks (RNN)

How does artificial neural network (ANN) work?

If you want to explore more about how ANN works, I recommend going through the below article: ANN can be used to solve problems related to: Artificial Neural Network is capable of learning any nonlinear function. Hence, these networks are popularly known as Universal Function Approximators.

How to speed up the training of a CNN model?

Watch the webinar replay. You can try knowledge transfer techniques, i.e. use a CNN pre-trained on a different task. For example if you are doing something related to computer vision try another model which is trained on Imagenet or Cifar datasets. This will both speedup your training and you might end up with a much better model.