Deep learning has vast ranging applications and its application in the healthcare industry always fascinates me. As a keen learner and a Kaggle noob, I decided to work on the Malaria Cells dataset to get some hands-on experience and learn how to work with Convolutional Neural Networks, Keras and images on the Kaggle platform. One of the many things I like about Kaggle is the immense knowledge it holds in the form of Kernels and Discussions. Taking cues and references from various kernels and experts really helped me get better at producing highly accurate results. Do look at other kernels and understand their approach to gain more insights for your own development and knowledge building.
Plenty has been written about deep learning frameworks such as Keras and PyTorch, and how powerful yet simple to use they are for constructing and playing with wonderful deep learning models. There are so many tutorials/articles already written about model architecture and optimizers-- the concept of convolution, max pooling, optimizers such as ADAM or RMSprop. What if, all you wanted, is a single function to pull automatically images from a specified directory on your disk, and give you back a fully trained neural net model, ready to be used for prediction? Therefore, in this article, we focus on how to use a couple of utility methods from the Keras (TensorFlow) API to streamline the training of such models (specifically for a classification task) with a proper data pre-processing. In the end, we aim to write a single utility function, which can take just the name of your folder where training images are stored, and give you back a fully trained CNN model.
Over the last decade, the use of artificial neural networks (ANNs) has increased considerably. With all the buzz about deep learning and artificial neural networks, haven't you always wanted to create one for yourself? In this Keras tutorial, we'll create a model to recognize handwritten digits. We use the keras library for training the model in this tutorial. Keras is a high-level library in Python that is a wrapper over TensorFlow, CNTK and Theano.
Over the last decade, the use of artificial neural networks (ANNs) has increased considerably. People have used ANNs in medical diagnoses, to predict Bitcoin prices, and to create fake Obama videos! With all the buzz about deep learning and artificial neural networks, haven't you always wanted to create one for yourself? In this tutorial, we'll create a model to recognize handwritten digits We use the keras library for training the model in this tutorial. Keras is a high-level library in Python that is a wrapper over TensorFlow, CNTK and Theano.
In a Convolution Neural Network(CNN) there are different types of architectures are created. Depending upon the use cases we need to use them. Here I use LeNet architecture for creating a face recognition model. I made some changes in the architecture to reach the desired accuracy by hit and trial. LeNet consists of 7 layers alternatingly 2 convolutional and 2 average pooling layers, and then 2 fully connected layers and the output layer with activation function softmax.