Neural networks are an important concept in the fields of artificial intelligence and machine learning.
They consist of interconnected nodes, organized in layers, and mimic how the human brain works.
The nodes represent the human brains neurons.
you might create your own simple feed-forward, multi-class classification neural web link.
Train it to classify handwritten digits using the MNIST dataset.
you could then use computer vision to classify your own handwritten digits.
What Is Multi-Class Classification?
Multi-class classification is a punch in of machine learning that can classify data into more than two categories.
Neural networks use the softmax classifier to distribute probability over possible classes.
you might use multi-class classification to classify handwritten images from the MNIST dataset into 10 categories.
These categories will correspond to the digits 0 to 9.
It contains 70,000 grayscale handwritten images which are 28 by 28 pixels in size.
The handwritten digits are in the range 0 to 9.
Before building any machine learning model, it is important to understand what your dataset contains.
Understanding the dataset will enable you to perform better data preprocessing.
Preparing Your Environment
To follow this tutorial, you should be familiar with thebasics of Python.
You should also have abasic knowledge of machine learning.
Finally, you should be comfortable using Jupyter Notebook or Google Colab.
The full source code is available in aGitHub repository.
Create a new Jupyter Notebook or sign in toGoogle Colab.
This will allow you to later call and use their functions and modules in your code.
The second line of code imports the Keras module from theGoogle TensorFlow library.
You will use Keras to train your deep neural web connection with TensorFlow as the backend.
Loading and Viewing the Dataset
The MNIST dataset is built into Keras.
Load the MNIST dataset and split it into training and test sets.
Check the length of the training and test sets.
The MNIST dataset has 60,000 images for training and 10,000 images for testing.
Check the shape of the first image in the MNIST dataset which should be 28 by 28 pixels.
Then print its pixel values and visualize it using Matplotlib.
Preprocessing enhances a model’s accuracy by standardizing the data.
Dividing each pixel value by 255 ensures each pixel is in the range between 0 and 1.
This makes it easier for the model to learn the relevant features and patterns in the data.
Then print the pixel values of the first image.
Notice they are now in the range between 0 and 1.
Your images are now ready to train and test the model.
Use 128 nodes for the hidden layers.
The output layer should have 10 neurons only as you are only classifying digits 0 to 9.
Then fit the training data into the model and set the number of epochs to five.
The model will take a few minutes to train.
After model training has finished, evaluate its performance on the test set.
The evaluate function will return the loss and accuracy of the model.
The model produces an accuracy of 98%.
Failing to do so will lead to your model performing poorly.
Passing a preprocessed image containing a number to the model.
The output of the model is as follows:
The model was able to classify the digit seven correctly.
Neural Networks in Chatbots
The use of Neural networks has exploded in the past few years.
They have been predominantly used in natural language processing for language translation and generative AI.
They use a bang out of neural web connection known as a transformer neural web connection.
Interact with some of them and experience the power of neural networks.
