Deeper into Deep Neural Networks

In the previous blog, we talked about how the Autoencoders in Keras can help us innovate and solve problems that do not appear solvable even after increasing hidden layers and training time. In that blog, we learned that different model architectures conjured through different insights can help us solve a problem statement elegantly and with much less complexity than blindly stacking up layer after layer. We saw this with a Computer Vision research example which was an Automated De-Blurring problem. Starting with a vanilla Convolutional Neural Network, we ended up witnessing how being smart, innovative and using our insights and instincts, we could solve what seemed like an unsolvable problem which was made possible by the flexibility of the Functional API in Keras. We called adding layers and increasing training time as the'go to' thing to do.