Deep Learning For Sequential Data – Part I: Why Do We Need It

~ Prateek Joshi ~ 4 Comments

Most of the current research on deep learning is focused on images. Deep learning is being actively applied to many areas, but image recognition is definitely generating a lot of buzz. Deep neural networks are being used for image classification tasks and they are able to outperform all the other approaches by a big margin. … Continue reading Deep Learning For Sequential Data – Part I: Why Do We Need It

How To Extract Feature Vectors From Deep Neural Networks In Python Caffe

~ Prateek Joshi ~ 6 Comments

Convolutional Neural Networks are great at identifying all the information that makes an image distinct. When we train a deep neural network in Caffe to classify images, we specify a multilayered neural network with different types of layers like convolution, rectified linear unit, softmax loss, and so on. The last layer is the output layer … Continue reading How To Extract Feature Vectors From Deep Neural Networks In Python Caffe

How To Programmatically Create A Deep Neural Network In Python Caffe

~ Prateek Joshi ~ 2 Comments

When you are working with Caffe, you need to define your deep neural network architecture in a '.prototxt' file. These prototxt files usually consist of hundreds of lines, defining layers and corresponding parameters. Before you start training your neural network, you need to create these files and define your architecture. One way to do this … Continue reading How To Programmatically Create A Deep Neural Network In Python Caffe

Understanding Locally Connected Layers In Convolutional Neural Networks

~ Prateek Joshi ~ 12 Comments

Convolutional Neural Networks (CNNs) have been phenomenal in the field of image recognition. Researchers have been focusing heavily on building deep learning models for various tasks and they just keeps getting better every year. As we know, a CNN is composed of many types of layers like convolution, pooling, fully connected, and so on. Convolutional … Continue reading Understanding Locally Connected Layers In Convolutional Neural Networks

What Is Local Response Normalization In Convolutional Neural Networks

~ Prateek Joshi ~ 12 Comments

Convolutional Neural Networks (CNNs) have been doing wonders in the field of image recognition in recent times. CNN is a type of deep neural network in which the layers are connected using spatially organized patterns. This is in line with how the human visual cortex processes image data. Researchers have been working on coming up … Continue reading What Is Local Response Normalization In Convolutional Neural Networks

Understanding Xavier Initialization In Deep Neural Networks

~ Prateek Joshi ~ 6 Comments

I recently stumbled upon an interesting piece of information when I was working on deep neural networks. I started thinking about initialization of network weights and the theory behind it. Does the image to the left make sense now? The guy in that picture is lifting "weights" and we are talking about network "weights". Anyway, when we implement … Continue reading Understanding Xavier Initialization In Deep Neural Networks

How Are Decision Trees Constructed In Machine Learning

~ Prateek Joshi ~ 6 Comments

Decision trees occupy an important place in machine learning. They form the basis of Random Forests, which are used extensively in real world systems. A famous example of this is Microsoft Kinect where Random Forests are used to track your body parts. The reason this technique is so popular is because it provides high accuracy with relatively little … Continue reading How Are Decision Trees Constructed In Machine Learning