Autoencoder Implementation on Tensorflow
Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learningWith the new Tensorflow API, it has become …
Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learningWith the new Tensorflow API, it has become …
It is a good idea to visualize the feature maps for a specific input image in order to to understand what features of the …
Recurrent Neural Network (RNN) model has been very useful to processing sequential data. Tensorflow Keras is a great platform to implement RNN as the …
Recurrent Neural Network (RNN) model has been very useful to predict time series data.. Training on Tensorflow Keras is a great platform to implement …
Entropy is the number of bits required to transmit a randomly selected event from a probability distribution. A skewed distribution has a low entropy, whereas …
Resnet and Mobilenet are the popular pre-trained models for computer visions. Renet is more accurate, while Mobilenet is much smaller in size. In this …
One way to achieve network compression is using depthwise separable convolution. Depthwise separable convolution is used in many pre-trained neural networks such as MobileNet, …