Digital Signal Processors take waveforms in voice, audio, video, temperature, and then mathematically manipulate them. The key idea of a DSP is to create complex, realistic signals by precisely controlling and tuning their many parameters.
Many machine learning models, including neural networks, consistently misclassify the adversarial examples. Adversarial examples are nothing but specialised inputs created to confuse neural networks, ultimately resulting in misclassification of the result. These notorious inputs are almost the same as the original image to human eyes but cause a neural network to fail to identify the image’s content.
Cell growth and division are two significant areas of research in the field of cell physiology. Of late, there have been several developments in microfluidic
Tesla has always had a unique approach towards self-driving cars. The electric car company has been developing Computer Vision and Synthetic Neural Networks to solve
Computer Vision or CV can be defined as a field of study that aims to develop techniques to enable computers to “see” or develop “vision”
Rapidly developing sensor technology and software processing has enabled autonomy for trucks — improving fleet operations by decreasing downtime, reducing personnel costs, crashes and fatalities.
A few days back, a naval architecture was launched, ‘XBNet’, which stands for ‘Extremely Boosted Neural Network’, which combines gradient boosted tree with a feed-forward neural network, making the model robust for all performance metrics.
MIT researchers have come out with a digital fabric. The fibre, embedded in a shirt, can detect, store, extract, analyse and communicate helpful information and
In this article we implement a character level recurrent neural network (RNN) from scratch in Python using NumPy.
Yann Lecun changed computer vision and, for that matter, artificial intelligence forever with CNNs.
PaddleSeg is an Image segmentation framework based on Baidu’s PaddlePaddle(Parallel Distributed Deep Learning). It provides high performance and efficiency, SOTA segmentation models optimized for MultiNode and MultiGPU production systems.
MultiSpeaker Text to Speech synthesis refers to a system with the ability to generate speech in different users’ voices. Collecting data and training on it for each user can be a hassle with traditional TTS approaches.
Quantization is the process of mapping the high precision values (a large set of possible values) to low precision values(a smaller set of possible values). Quantization can be done on both weights and activations of a model.
KGCNN offers a straightforward and flexible integration of graph operations into the Tensorflow-Keras framework using RaggedTensors.
In a major breakthrough, researchers at OpenAI have discovered neural networks within AI systems resembling the neural network inside the human brain. The multimodal neurons
Graph Neural Networks (GNNs) is a subtype of neural networks that operate on data structured as graphs. In an article covered earlier on Geometric Deep
The researchers at the Massachusetts Institute of Technology (MIT) have developed ‘liquid network’, a neural network that can learn on the job. Liquid networks can
Presently, one of the active ongoing challenging problems always prevails to the engineers and researchers is to design the numerical simulation models for the turbulent flow.
The Adversarial Robustness Toolbox(ART) is a Python library which is one of the complete resources providing developers and researchers for evaluating the robustness of deep neural networks against adversarial attacks
A Recurrent Neural Network or RNN is a popular multi-layer neural network that has been utilised by researchers for various purposes including classification and prediction.
“Neural networks are really good at knowing the right answer 99% of the time. But 99% won’t cut it when lives are on the line.”
A Beginner’s Guide To Neural Network Modules In Pytorch
writing a brief code that would explain it and then would give a simple explanation
Convolutional Neural Networks (CNNs) are one of the most important neural network algorithms in the present scenario. Tech giants like Google, Facebook, Amazon have been
In this article, I’ll discuss the various types of activation functions present in a neural network.
Machine learning is mostly about building good function approximations from data. And, when it comes to good approximations, deep learning algorithms have a great following
Neural networks are a series of algorithms that identify underlying relationships in a set of data. These algorithms are heavily based on the way a
It is also termed as ConvNet that is a Deep learning algorithm that inputs an image, draws different feature maps using different kernels that allocates
“According to the filing, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks.” Looks like Google won’t be
Since the explosion of AlexNet paper onto the computer vision scene, machine learning results have improved greatly due to deeper models with high complexity, increased
In this article, we will:
Learn the different techniques to avoid overfitting of the model and Implement these techniques to a deep learning model
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