Consider an example where you are trying to classify a car and a bike. If an image of a truck is shown to the network, it ideally should not predict anything. But, because of the softmax function, it assigns a high probability to one of the classes and the network wrongly, though confidently predicts it to be a car. In order to avoid this, we use the Bayesian Neural Network (BNN).
Skorch is one of the useful libraries in Pytorch to work on machine learning models especially neural networks. It is a robust library that works
Handwritten Character Digit Classification using Neural Network – EMNIST Handwritten alphabet digit recognition using deep learning
Computer vision (CV) is the field of study that helps computers to study using different techniques and methods so that it can capture what exists
Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems
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.
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.
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.
There are a variety of convolutional neural networks and all have their own advantage. With the varying architectures, these models have shown an overwhelming performance
In this article, we will create an ensemble of convolutional neural networks. In this experiment, we will create an ensemble of 10 CNN models and this ensemble will be applied in multi-class prediction of MNIST handwritten digit data set.
In this article, we will recognize the fruit where the Convolutional Neural Network will predict the name of the fruit given its image. We will train the network in a supervised manner where images of the fruits will be the input to the network and labels of the fruits will be the output of the network. After successful training, the CNN model will be able to correctly predict the label of the fruit.
In this article, we will discuss fashion apparel recognition using the Convolutional Neural Network (CNN) model. To train the CNN model, we will use the Fashion MNIST dataset. After successful training, the CNN model can predict the name of the class a given apparel item belongs to.
On the eve of PAC-MAN’s 40th anniversary, the researchers at the prominent tech giant, NVIDIA, teamed up with MIT, the University of Toronto and Vector
In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. The classification accuracies of the VGG-19 model will be visualized using the non-normalized and normalized confusion matrices.
At the present scenario, video games portray a crucial role when it comes to AI and ML model development and evaluation. This methodology has been
The realm of possibilities for artificial intelligence may have broadened over the last few years, but can it program a machine to be funny? Given
In yet another effort to shed the image of black box models for machine learning, a group of researchers probed something fundamental this time –
OpenAI has recently launched Microscope in order to help researchers understand the architecture and behaviour of neural networks in a better way. According to the
With so much innovation happening in the tech space, early adoption of sports-tech at the consumer level has started. According to reports, the global sports
When searching for a new material which can be used for different applications such as batteries or other energy-related devices, the possibilities are endless. There
In this article, we will discuss the Long-Short-Term Memory (LSTM) Recurrent Neural Network, one of the popular deep learning models, used in stock market prediction. In this task, we will fetch the historical data of stock automatically using python libraries and fit the LSTM model on this data to predict the future prices of the stock.
Neural networks can perform well on tasks such as image classification, language translation, and game playing. However, they usually fail to perform well in tasks
The primary task of a Deep Neural Network – especially in case of Image recognition, Video Processing etc is to extract the features in a
The popularity of compression techniques grew with the increasing sizes of machine learning models, which grew in order to cater to the growing number of
AI research startup DeepMind has now open-sourced new libraries for neural networks and reinforcement learning based on JAX. JAX is a numerical computing library launched
The problem of space debris has reached a critical point as innovators continue to send satellites into space, which are never brought back. There are
A healthy child’s brain under development is capable of adding nearly 250,000 neurons every minute! At birth, a brain has almost all the neurons that
Artificial Neural Network (ANN) is probably the first stop for anyone who enters into the field of Deep Learning. Inspired by the structure of Natural
As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications such
Image processing has been utilised in the healthcare sector for years to understand different parts of living things. In the healthcare sector, images are a
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