In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The pictures here are from the full article. Source code is also provided.

Before you continue, make sure you understand how a convolutional neural network works. For example,

• What is a convolutional layer, and what is the filter of this convolutional layer?
• What is an activation layer (ReLu layer (most widely used), sigmoid activation or tanh)?
• What is a pooling layer (max pooling / average pooling), dropout?
• How does Stochastic Gradient Descent work?

The contents of this blog-post is as follows:

1. Tensorflow basics:

• Constants and Variables
• Tensorflow Graphs and Sessions
• Placeholders and feed_dicts

2. Neural Networks in Tensorflow

• Introduction
• Loading in the data
• Creating a (simple) 1-layer Neural Network:
• The many faces of Tensorflow
• Creating the LeNet5 CNN
• How the parameters affect the outputsize of an layer
• Adjusting the LeNet5 architecture
• Impact of Learning Rate and Optimizer

3. Deep Neural Networks in Tensorflow

• AlexNet
• VGG Net-16
• AlexNet Performance

4. Final words

To read this blog, click here. The code is also available in my GitHub repository, so feel free to use it on your own dataset(s).