This document summarizes a convolutional neural network (CNN) that was implemented using Keras to recognize handwritten digits from 0-9. The CNN model contains steps for convolution, ReLU activation, pooling, flattening, and fully connected layers. The model was trained on a dataset of handwritten digits and achieved 97% accuracy on the test set, demonstrating CNNs capabilities for image classification tasks. The project utilized common deep learning libraries like NumPy, Keras, TensorFlow and followed typical CNN architecture of feature extraction via convolution and pooling layers followed by classification with dense layers.

1. A Neural Network that Understands
Handwriting
EFFORTS BY:
SHIVAM RAMAN SAWHNEY

2. BREIF
The presentation highlights a convolutional neural network (CNN)
that understands handwritten digits from 0-9.
Keras library was used for implementation of the CNN.
Also, the presentation in brief will tell you about:
The Code
Logic
Method used

3. CONTENTS
Important Notice
What are Neural Networks.?
Convolutional Neural Networks
Project Outline
Procedure
Outlook of Project

4. For running of this project python needs to be installed along with
some of its core libraries:
NumPy
Keras
Scikit learn
TensorFlow

5. What are Neural Networks.?
• It is basically a computer system modelled on the human brain and
nervous system.
• Like in our brain; we have numerous “neurons” that are
interconnected with one another, in a similar analogy we have
“neurons” in Neural Networks.
• They are basically of two types:
Artificial Neural Networks. (ANN)
Convolutional Neural Networks. (CNN)

6. Convolutional Neural Networks. (CNN)
• A convolutional neural network is a class of deep, feed-forward
artificial neural networks, most commonly applied to analysing
visual imagery.
• They have applications in image and video
recognition, recommender systems and natural language
processing.
More info:
https://en.wikipedia.org/wiki/Convolutional_neural_network

7. Project Outline
• Step 1: Convolution Operation.
• Step 2: ReLU Layer.
• Step 3: Pooling.
• Step 4: Flattening.
• Step 5: Full Connection.
Pool
Flatten
Full
Connection

8. Procedure:
• In the convolution step; the input image is taken as a matrix with
numbers denoting feature intensity.
• Then it is passed through a feature matrix (for a distinct
feature),which takes out the essential features from the image.
• This basically reduces the size of the input matrix which is beneficial
for upcoming steps.

9. • WE CREATE MANY
FEATURE MAPS TO
OBTAIN OUR FIRST
CONVOLUTION LAYER.

10. • Pooling (here we perform max pooling) further reduces the size of
our CNN by extracting the max value of features from the convolution
layer that we have formed.
• Flattening basically flattens our pooled matrix into a column matrix
so as to serve as an input for our ANN.
• The final step of making the ANN and joining it further with the
above steps is called Full Connection.

11. BASIC OUTLOOK OF THE PROJECT

12. • These two images show the
importing of libraries that are
required for the project and
the main step of making the
CNN and shows the 5 steps
stated earlier.

13. • This step trains our dataset ( which is imported by the keras library).
• The basic fundamentals of neural networks is that we split our
dataset into training and testing; and then by training our model on
the dataset, predict the corresponding results on the testing data.

14. • Our model gives us an accuracy of 97% which is quite remarkable. This
shows us that CNN and neural networks have quite a future in the world of
“Image classification”
• For any further queries feel free to drop a message at my LinkedIn profile:
https://www.linkedin.com/in/shivam-sawhney17/