Tutorials

Introduction to Recurrent Neural Networks

In typical neural networks, all the inputs and outputs are independent of each other, Which means each hidden layer has its separate set of...

Image Data Augmentation in Keras

When dealing with Deep Learning on images, the first question that arises in your mind is where you will get this particular type of...

Transfer Learning in Keras (Image Recognition)

Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for...

Generating Your Shakespeare Text Using Sequential Models Such As Long-Short-Term-Memory (LSTMs), Gated Recurrent Units (GRUs), Recurrent Neural Network (RNNs)

In the previous article, we discussed Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and applied them to detect fake news. This article will...

Introduction to Support Vector Machines (SVMs)

Support Vector Machines (SVMs) are supervised learning models for classification and regression problems. Support Vector Machines(SVMs) are supervised learning models for classification and regression...

Fake News Detection Using Word Embeddings, Artificial Neural Networks, and Convolutional Neural Networks

In this article, we will learn how to use Deep Learning models for NLP. Since the model takes numerical vectors as input, we need...

Optimizing Hyperparameters Using The Keras Tuner Framework

Hyperparameter optimization is an integral part of deep learning as a machine learning project is crucially dependent on the choice of good hyperparameters. Neural...

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...

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