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 classification problem,...
“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 understanding the concepts of NLP, we will...
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, semantic segmentation,...
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 to finding...
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 architectures that...
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 PyTorch is...
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 an automatic...
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 straightforward manner....
Getting Started With Pytorch In Google Collab With Free GPU
In this post, we will discuss the basics of pytorch by using the google colab gpu
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 building a Recurrent...