In this article, we’ll be learning what is Convolutional Neural Network (CNN) and implement it for Malaria Cell Image dataset. I’ve got the dataset from Kaggle.
CNN is a multilayer perceptron which is good at identifying patterns within datasets. It uses mathematics to extract important features of data to make further classification. As these networks are good with pattern recognition, they are mostly used with images. It could also work with other data but the condition is that data should be in a sequence i.e. shuffling this data must change its meaning.
To understand CNNs in detail, we need to…
In this article, we’ll try to understand some basic concepts related to Natural Language Processing (NLP). I will be focusing on the theoretical aspects over programming practices.
Why should one pre-process text, anyway? It is because computers are best at understanding numerical data. So, we convert strings into numerical form and then pass this numerical data into models to make them work.
We’ll be looking into techniques like Tokenization, Normalization, Stemming, Lemmatization, Corpus, Stopwords, Part of speech, a bag of words, n-grams, and word embedding. These techniques are enough to make a computer understand data with the text.
In this article, we’ll try to implement a program that will be used as a quote suggestion system. I’ll be using quotes from https://programming-quotes-api.herokuapp.com/ API. The software should return similar quotes if the user liked a quote and return should return different quotes if the user disliked a quote.
We’ll need some sort of method to check the relativity of every quote with each other. The sentiment of sentences is used to find the most related quote.
To complete the requirements, we’ll be using two AWS services.
In this article, we’ll be learning about using machine learning on sequential data. Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) are two types of networks that could be used for this purpose. Lastly, we’ll implement one TensorFlow model from scratch using the IMDB dataset.
Its applications are very wide including chatbots, translators, text generators, sentiment analysis, speech recognition and so on…
But first, let’s understand sequential data in detail so that we are on the same page. Sequential data could be any data that is dependent on the previous version of it. For example, text data in…
In the previous article, we learned the theoretical concepts of Generative Adversarial Networks. Please check out this blog on “Scratching the surface of Generative Adversarial Network”.
In this article, we’ll understand how to implement Generative Adversarial Networks in Tensorflow 2.0 using the MNIST dataset of digits. Our model should be able to accurately create images similar to MNIST dataset images. Image from MNIST dataset looks like this:
There are many sites in the present date that provide house rental services. Customers can rent a place from owners directly from the website. The challenge for such companies is to decide the perfect price for a place. These companies use ML to predict the price of place based on the information provided.
In this notebook, we will try to replicate the model used by these websites and understand the data science techniques used. We’ll use Melbourne house prices dataset from kaggle.
Predict the price of rental places based on other information like the number of rooms, land size…