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Dr. Vaibhav Kumar

Dr. Vaibhav Kumar

Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.
ResNet50 in PyTorch with TPU
Developers Corner

Hands-On Guide to Implement ResNet50 in PyTorch with TPU

In this article, we will demonstrate the implementation of ResNet50, a Deep Convolutional Neural Network, in PyTorch with TPU. The model will be trained and tested in the PyTorch/XLA environment in the task of classifying the CIFAR10 dataset. We will also check the time consumed in training this model in 50 epochs.

selftime feature
Developers Corner

Recurrent Neural Network in PyTorch for Text Generation

In this article, we will train a Recurrent Neural Network (RNN) in PyTorch on the names belonging to several languages. After successful training, the RNN model will predict names belonging to a language that start with an input alphabet letter.

Developers Corner

Multi-Class Text Classification in PyTorch using TorchText

In this article, we will demonstrate the multi-class text classification using TorchText that is a powerful Natural Language Processing library in PyTorch. For this classification, a model will be used that is composed of the EmbeddingBag layer and linear layer. The EmbeddingBag deals with the text entries with varying length by computing the mean value of the bag of embeddings. This model will be trained on the DBpedia dataset with texts belonging to the 14 classes. After successful training, the model will predict the class label for the input text. 

Recurrent Neural Network in PyTorch
Developers Corner

Name Language Prediction using Recurrent Neural Network in PyTorch

In this article, we will demonstrate the implementation of a Recurrent Neural Network (RNN) using PyTorch in the task of multi-class text classification. This RNN model will be trained on the names of the person belonging to 18 language classes. After successful training, the model will predict the language category for a given name that it is most likely to belong. 

CNN Using PyTorch With TPU
Developers Corner

How To Implement CNN Model Using PyTorch With TPU

This article demonstrates how we can implement a Deep Learning model using PyTorch with TPU to accelerate the training process. Here, we define a Convolutional Neural Network (CNN) model using PyTorch and train this model in the PyTorch/XLA environment. XLA connects the CNN model with the Google Cloud TPU (Tensor Processing Unit) in the distributed multiprocessing environment. In this implementation, 8 TPU cores are used to create a multiprocessing environment.

Developers Corner

Hands-On guide To Neural Style Transfer using TensorFlow Hub Module

In this article, we present a very fast and effective way to neural style transfer in images using the TensorFlow Hub module. The TF-Hub module provides the pre-trained VGG Deep Convolutional Neural Network for style transfer. This approach takes less than four seconds to transfer style to a content image.

fake job classification
Developers Corner

Classifying Fake and Real Job Advertisements using Machine Learning

In this article, we will train the machine learning classifier on Employment Scam Aegean Dataset (EMSCAD) to identify the fake job advertisements. First, we will visualize the insights from the fake and real job advertisement and then we will use the Support Vector Classifier in this task which will predict the real and fraudulent class labels for the job advertisements after successful training. Finally, we will evaluate the performance of our classifier using several evaluation metrics. 

foreign exchange rate prediction
Developers Corner

Foreign Exchange Rate Prediction using LSTM Recurrent Neural Network

In this article, we will implement the LSTM Recurrent Neural Network to predict the foreign exchange rate. The LSTM model will be trained to learn the series of previous observations and predict the next observation in the sequence. We will apply this model in predicting the foreign exchange rate of India.

fake news classification
Developers Corner

Hands-On Guide to Predict Fake News Using Logistic Regression, SVM and Naive Bayes Methods

In this article, we will train the machine learning classifiers to predict whether given news is real news or fake news. For this task, we will train three popular classification algorithms – Logistics Regression, Support Vector Classifier and the Naive-Bayes to predict the fake news. After evaluating the performance of all three algorithms, we will conclude which among these three is the best in the task.

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