Word Embedding and NLP with TF2.0 and Keras on Twitter Sentiment Data

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…

Classify Dog or Cat by the help of Convolutional Neural Network(CNN)

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…

Text Generation using Tensorflow, Keras and LSTM

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…

Multi-step-Time-series-predicting using RNN LSTM

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 2.0 Tutorial – Building Your First ANN with TensorFlow 2.0

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…