Convolutional Neural Networks with TensorFlow

This chapter will guide how to construct and implement Convolutional Neural Networks(CNNs) in Python with the TenorFlow framework.

Below items should be learnt in this chapter:

• [ ] What are tensors?
• [ ] How tensors differ from matrices?
• [ ] How even a single line of code is implemented via a computational graph in TensorFlow?
• [ ] What are constants, variables and placeholders?
• [ ] How to implement Convolutional Neural Network?
• How to use Python and its libraries to load, explore and analyze data?
• How to visualize images as a matrix?
• How to reshape the data and rescale the images between 0 and 1?
• [ ] How to construct the deep neural network model?
• How to define the network parameters?
• How to create wrappers to increase the simplicity of the code?
• How to define weights and biases?
• how to model the network?
• How to define loss and optimizer nodes?
• How to train and test the model?