# AI News, Improving the Performance of a Neural Network

- On Wednesday, June 6, 2018
- By Read More

## Improving the Performance of a Neural Network

Your friend goes on memorising each formula, question and answer from the textbook but you, on the other hand, are smarter than him, so you decide to build on intuition and work out problems and learn how these formulas come into play.

Test day arrives, if the problems in the test paper are taken straight out of the textbooks, then you can expect your memorising friend to do better on it but, if the problems are new ones that involve applying intuition, you do better on the test and your memorising friend fails miserably.

E.x: In a convolutional neural network, some of the hyperparameters are kernel size, the number of layers in the neural network, activation function, loss function, optimizer used(gradient descent, RMSprop), batch size, number of epochs to train etc.

You might ask, “there are so many hyperparameters, how do I choose what to use for each?”, Unfortunately, there is no direct method to identify the best set of hyperparameter for each neural network so it is mostly obtained through trial and error.

You can choose different neural network architectures and train them on different parts of the data and ensemble them and use their collective predictive power to get high accuracy on test data.

When combining different cats vs dogs classifiers, the accuracy of the ensemble algorithm increases based on the Pearson Correlation between the individual classifiers.

After performing all of the techniques above, if your model still doesn’t perform better in your test dataset, it could be ascribed to the lack of training data.

Data Augmentation Techniques If you are working on a dataset of images, you can augment new images to the training data by shearing the image, flipping the image, randomly cropping the image etc.

- On Tuesday, June 18, 2019

**Training/Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7**

Welcome to part seven of the Deep Learning with Neural Networks and TensorFlow tutorials. We've been working on attempting to apply our recently-learned ...

**How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)**

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to ...

**Deep Compression, DSD Training and EIE**

Deep Compression, DSD Training and EIE: Deep Neural Network Model Compression, Regularization and Hardware Acceleration Neural networks are both ...

**Train, Test, & Validation Sets explained**

In this video, we explain the concept of the different data sets used for training and testing an artificial neural network, including the training set, testing set, and ...

**Lecture 15 | Efficient Methods and Hardware for Deep Learning**

In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning ...

**Training - Using Convolutional Neural Network to Identify Dogs vs Cats p. 3**

Now, the training data and testing data are both labeled datasets. The training data is what we'll fit the neural network with, and the test data is what we're going ...

**Capsule Networks: An Improvement to Convolutional Networks**

Only a few days left to signup for my Decentralized Applications course! Geoffrey Hinton (who popularized backpropagation in the 80s) ..

**Lecture 3 | Loss Functions and Optimization**

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...

**Lecture 11 | Detection and Segmentation**

In Lecture 11 we move beyond image classification, and show how convolutional networks can be applied to other core computer vision tasks. We show how ...

**Weka Tutorial 35: Creating Training, Validation and Test Sets (Data Preprocessing)**

The tutorial that demonstrates how to create training, test and cross validation sets from a given dataset.