Machine learning and statistics with python

A brief history of CNN: Convolutional neural networks is one of the most famous neural network architectures which are currently used in deep learning practices and researches. The concept of CNN came into existence from the long line of research which all started with the finding that animal brains have visual receptors with high local similarities of their reception and high overlap of their observation area. This then originated the concept of neurocognitron; and slowly through a decades of research the other concepts related to cnn, such as convolutional layer, spatial invariance, pooling layer and other properties came into picture. In the beginning of 2000, K.chellapia et al. created the final breakthrough; which was a gpu implementation of CNN. This made people realize that how with immense computational possibilities CNNs can come up to great works. There have been many development upon different breakthrough researches. We will discuss them later in this article. What is CN...

 Introduction: All of us, when start out with neural network, go through pictures of networks, try and understand complex equations; get baffled by the back-propagation equations and take our time to eventually assimilate the knowledge of what all that stands for. Recently while mentoring students for our new effort mentorbruh , one of my students asked some pretty interesting yet very basic questions. This content is an effort to write down those so that it can help other students who are going through neural networks first time and have these doubts. But before that, let's brush up the basics once pretty quickly.   Abstract of this article: In this post, we are going to take small steps in explaining what is neural network, what are input,output and hidden layers; how does a node calculate its values. We will also briefly touch the concepts of bias, activation, hidden layer number count and all the related artifacts. In the more looser second part of this neural network b...

 Introduction:                       Photo by CHUTTERSNAP on Unsplash   Spacy is the new age, industrial usage and computationally economical nlp library which employs full pipelines for nlp works and attempts to democratize the natural language processing for smaller companies by publishing high end work into the open source software spacy. For last 2 months, I have been reading and using spacy heavily and been blogging about it.  We have seen a detailed how to use spacy in the part1 , part2 , part3 and part4 of the spacy series. But as we have worked mostly with one model; we have never dealt with different pipelines the spacy library offers. So in this post, I am going to give a small summary of the different models we can use for spacy and what are the pipelines related to them. Let's dig in. How to download spacy models and use them: All spacy pipelines are d...

 Introduction: We have discussed different aspects of spacy in part 1 , part 2 and part 3 . Now, up to this point, we have used the pre-trained models. Now, in many cases, you may need to tweak or improve models; enter new categories in the tagger or entity for specific projects or tasks. In this part, we will discuss how to modify the neural network model or train our own models also; as well as different technical issues which arise in these cases. How does training works: 1. initialize the model using random weights, with nlp.begin_training.  2. predict a bunch of samples using the current model by nlp.update 3. Compare prediction with true labels, calculate change of weight based on those predictions and finally update the weights. 4. finally reiterate from 2. Refer to the following diagram for a better understanding: Now in practice, the goal is to create annotated data using prodigy, brat or easily using phraseMatcher feature of spacy. Using phraseMatcher we can quick...