Elif Sertel’s Post

PCA has many different use cases in machine learning. One popular use case is that it can reduce the number of parameters in a Multilayer Perceptron (MLP) neural network model... https://lnkd.in/g9gJgrJ7

One of the cons of neural networks are that they are hard to understand and make sense of (e.g. comparing interpretability of deep learning model with 100,000 neurons vs linear regression models) eventhough they generally model the relationship between inputs and output(s) very well. This might soon change since researchers from MIT have found a new approach to model the neuron networks, it is called “Liquid neuron network”. This new models allow for extraction of model decision making logics into the form of decision tree and allow for easier interpretation by human and also with comparable or even better performance than popular model such as LSTM! Check out the video below if you are interested. Link: https://lnkd.in/g6DpXyDj

Embarking on a journey through the intricate world of Recurrent Neural Networks (RNNs) opens up a universe where machines mimic human-like sequential processing abilities. Read more 👉 https://lttr.ai/ARAaU #ArtificialNeuralNetwork #PredictSequentialData #SignificantMilestone #EndowMachines #NaturalLanguageProcessing #RecurrentNeuralNetworks #RecurrentNeuralNetwork #AnalyzingSequentialData #ProcessingSequentialData #TimeSeriesData

I am thrilled to share with you that I just published my first medium article on Recurrent Neural Networks (RNNs), Discovering the strengths and limitations of RNNs and exploring their different types and architectures. Article Link: https://lnkd.in/e5QTXreK #ai #machinelearning #neuralnetwork #datascience

Regularization in Image Recognition: Taming the Complexity of Convolutional Neural Networks 🌟 Exciting News! 🌟 📣Check out our latest blog post on "Regularization in Image Recognition: Taming the Complexity of Convolutional Neural Networks". 🎉 🔬 In this article, we dive deep into the world of Convolutional Neural Networks (CNNs) and explore how regularization techniques can help overcome overfitting, a common challenge in image recognition. From L1 and L2 regularization to dropout, batch normalization, data augmentation, and early stopping, we cover it all! 💡💪 🔗 Follow the link below to gain valuable insights and take your image recognition skills to the next level! 👇 🔗 Read the full article here: [Regularization in Image Recognition: Taming the Complexity of Convolutional Neural Networks](https://ift.tt/FLImdtn) Don't miss out on the chance to enhance your image recognition techniques! 📸✨ #imageRecognition #ConvolutionalNeuralNetworks #Regularization #MachineLearning #DataScience https://ift.tt/FLImdtn

A Convolutional Neural Network (CNN) is a type of Deep Learning neural network architecture commonly used in Computer Vision. Computer vision is a field of Artificial Intelligence that enables a computer to understand and interpret the image or visual data. When it comes to Machine Learning, Artificial Neural Networks perform really well. Neural Networks are used in various datasets like images, audio, and text. Different types of Neural Networks are used for different purposes, for example for predicting the sequence of words we use Recurrent Neural Networks more precisely an LSTM, similarly for image classification we use Convolution Neural networks. In this blog, we are going to build a basic building block for CNN. #snsinstitutions #snsct #snsdesignthinkers #snsdesignthinking

Discover how to build neural networks that can transform data into decisions. 🧠 Dive into the blog article here: https://lnkd.in/dYQn8xJ9 #neuralnetworks #machinelearning #codelabsacademy

Loss Functions in Neural Networks: From Binary Classification to Multi-class Problems 🎉 Exciting News! 🎉 Loss Functions in Neural Networks: From Binary Classification to Multi-class Problems Neural networks have revolutionized machine learning and artificial intelligence, empowering us to unravel complex patterns and make accurate predictions. And at the core of neural networks lies the loss function, that measures the disparity between predicted and actual values. What's fascinating is that the choice of loss function is tailored to the type of problem at hand, be it binary classification or multi-class classification. 🔒 Discover the power of different loss functions in neural networks and their suitability to diverse problem domains. From binary cross-entropy loss and hinge loss to categorical cross-entropy loss and sparse categorical cross-entropy loss, this article explores them all! 📚 Join the conversation now by checking out the full blog post: [Link Here!](https://ift.tt/qLAfcio) 🧠💡 InstaDataHelp Analytics Services #neuralnetworks #lossfunctions #binaryclassification #multiclassclassification #machinelearning #deeplearning #blogpost #analytics https://ift.tt/qLAfcio

Exciting news: Our paper, "Convolutional Neural Network (CNN) to Reduce Construction Loss in JPEG Compression Caused by Discrete Fourier Transform (DFT)," is now published! 📚📷 We tackled the challenge of improving JPEG compression in the face of DFT's periodic artifacts. Leveraging Convolutional Neural Networks (CNNs), we achieved better feature extraction and more efficient image compression. 🖼️🚀 #ImageCompression #ResearchPublication https://lnkd.in/gzNnnd7f