How to use Pre-trained VGG16 models to predict object The VGG network architecture was introduced by Simonyan and Zisserman in their 2014 paper, Very Deep Convolutional Networks for Large Scale Image Recognition. This network is characterized by its simplicity, using only 3×3 convolutional layers stacked on top of each other Read more…
Word Embedding and Sentiment Analysis What is Word Embedding? Natural Language Processing(NLP) refers to computer systems designed to understand human language. Human language, like English or Hindi consists of words and sentences, and NLP attempts to extract information from these sentences. Machine learning and deep learning algorithms only take numeric Read more…
Multi-Label Image Classification in Python In this project, we are going to train our model on a set of labeled movie posters. The model will predict the genres of the movie based on the movie poster. We will consider a set of 25 genres. Each poster can have more than Read more…
What is CNN This Notebook demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. Unlike traditional multilayer perceptron architectures, it uses two operations Read more…
Breast Cancer Detection Using CNN in Python Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018, making it a significant health problem in present days. The key challenge in breast cancer detection is Read more…
Use of Dropout and Batch Normalization in 2D CNN on Dog Cat Image Classification in TensorFlow 2.0 We are going to predict cat or dog by the help of Convolutional neural network. I have taken the dataset from kaggle https://www.kaggle.com/tongpython/cat-and-dog. In this dataset there is two class cats and dogs Read more…
Classification using CNN It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In this project we are going to build a model using CNN which predicts if the transaction is genuine Read more…
Automatic Text Generation Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the Read more…
Combining NLP Models and Creation of Custom rules using SpaCy Objective: In this article, we are going to create some custom rules for our requirements and will add that to our pipeline like explanding named entities and identifying person’s organization name from a given text. For example: For example, the Read more…
Feature Selection and CNN In this project we are going to build a neural network to predict if a particular bank customer is satisfies or not. To do this we are going to use Convolutional Neural Networks. The dataset which we are going to use contains 370 features. We are going Read more…
Prediction of number of passengers for an airline using LSTM In this project we are going to build a model to predict the number of passengers in an airline. To do so we are going to use Recurrent Neural Networks, more precisely Long Short Term Memory. Recurrent Neural Network Neural Networks are Read more…
NLP Tutorial – GloVe Vectors Embedding with TF2.0 and Keras GloVe stands for global vectors for word representation. It is an unsupervised learning algorithm developed by Stanford for generating word embeddings by aggregating a global word-word co-occurrence matrix from a corpus. The resulting embeddings show interesting linear substructures of the word in Read more…
Prediction of Human Activity In this project we are going to use accelometer data to train the model so that it can predict the human activity. We are going to use 2D Convolutional Neural Networks to build the model. source = “Deep Neural Network Example” by Nils Ackermann is licensed under Creative Commons CC Read more…
Household Power Consumption Prediction using RNN-LSTM Power outage accidents will cause huge economic loss to the social economy. Therefore, it is very important to predict power consumption. Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of Read more…
Deep learning with Tensorflow # pip install tensorflow==2.0.0-rc0 # pip install tensorflow-gpu==2.0.0-rc0 Watch Full Lesson Here: Objective Our objective for this code is to build to an Artificial neural network for classification problem using tensorflow and keras libraries. We will try to learn how to build a nerual netwroks model Read more…
Classification using Fashion MNIST dataset What is TensorFlow? TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between Read more…
Sentiment Classification Using DistilBERT Problem Statement We will use the IMDB Movie Reviews Dataset, where based on the given review we have to classify the sentiments of that particular review like positive or negative. The motivational BERT BERT became an essential ingredient of many NLP deep learning pipelines. It is considered Read more…
Prediction of Google Stock Price using RNN In this we are going to predict the opening price of the stock given the highest, lowest and closing price for that particular day by using RNN-LSTM. Ref- https://colah.github.io/posts/2015-08-Understanding-LSTMs/ What is RNN? Recurrent Neural Networks are the first of its kind State of the Read more…
An Introduction to BERT Problem Statement We will use the IMDB Movie Reviews Dataset, where based on the given review we have to classify the sentiments of that particular review like positive or negative. Introduction Chatbots, virtual assistants, and dialog agents will typically classify queries into specific intents in order Read more…
Sentiment Classification in Python In this notebook we are going to implement a LSTM model to perform classification of reviews. We are going to perform binary classification i.e. we will classify the reviews as positive or negative according to the sentiment. Recurrent Neural Network Neural Networks are set of algorithms Read more…