Thinking-data-science-a-data-science-practitioners-guide

16 1 Data Science Process
Fig. 1.12 Hyper-parameter
tuning process
Model Building Using ANN
As you know, the design of ANN comes from the structure of a human brain. Like
our brain, the ANN comprises several layers, each layer containing several nodes
(neurons in our brain). We show a typical ANN architecture in Fig. 1.13.
Your job while designing an ANN architecture is to decide on the number of
layers, the type of connections between the layers, and the number of nodes in each
layer. We show the complete workflow for ANN development in Fig. 1.14.
After you define the architecture, feed the data to the network; let it propagate
through the network and produce some results. Based on these results, the network
will do the back propagation to fine-tune the hyper parameters (the weights assigned
to each link). This is again an iterative process and after several training iterations,
so-called epochs in ML terms, the network will provide you with a good level of
accuracy. If the accuracy scores are not acceptable, try feeding in more data for
training so that the network understands your data better. If this does not work, go
back to the drawing board and adjust the number of layers, number of nodes in each
layer, the optimization algorithms and the error functions that you have previously
set for network learning, and so on. I will describe this entire process when I discuss
the model development based on ANN in later chapters.
Though you may design your own ANN architecture for small applications and
medium-sized datasets, creating a huge architecture is not simple. Such architectures
are resource intensive and require training times spanning over several weeks. Image
recognition and object detection models like R-CNN and YOLO were developed on
complex architectures requiring enormous resources and training times. Such networks
are called deep neural networks. Can we use their pre-trained models for our
own purpose? The answer to this question is a big yes.
Models Based on Transfer Learning
To understand the term transfer learning, let me take an example. We have
pre-trained image classification models that can detect a dog in an image. What if
I want to know about the dog’s breed? Such models, as they detect hundreds of