COCO – A Definitive Dataset For Deep Learning On Images

In Deep Learning, the biggest challenge is to collect data. We need a massive dataset to train our model. Like in the...

“Hello World” of Natural Language Processing (NLP) Auto Correction

Introduction: Natural language processing (NLP) is the field of artificial intelligence that relates linguistics to computer science. After...

Visualizing CNN Models Through Gradient Weighted Class Activation Mappings

Convolutional Neural Networks(CNNs) and other deep learning networks have enabled extraordinary breakthroughs in computer vision tasks from image classification to object detection,...

Introduction to Reinforcement Learning

Reinforcement learning is a field of machine learning wherein the goal is learning to perform specific actions in an environment which leads...

Introduction to GANs (Generative Adversarial Networks)

Generative Adversarial Networks, or GANs, belong to generative models, which create new data instances that resemble the training data. GANs are algorithmic...

PyTorch versus TensorFlow

There is a vast array of deep learning frameworks, and many of them are viable tools, but the duopoly of TensorFlow and...

Introduction to PyTorch

PyTorch is a Python-based scientific computing package that is a replacement for NumPy to use the power of GPUs and TPUs and...

Introduction to TensorFlow

TensorFlow is an open-source software library designed by the Google team to facilitate machine learning and deep learning concepts in the most...

Implementing Batching for Seq2Seq Models in Pytorch

In this tutorial, we will discuss how to implement the batching in sequence2sequene models using Pytorch. We will implement batching by...

Classifying the Name Nationality of a Person using LSTM and Pytorch

Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. In the case of Convolution Neural Networks (CNN), the output from the softmax layer in the context of image classification is entirely independent of the previous input image.
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