Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 2, Convolutional Neural Network

From: https://towardsdatascience.com/only-numpy-medical-denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-2-6def73cabba5 So today, I will continue on the image denoising series, and fortunately I found this paper “Low-dose CT denoising with convolutional neural network. In Biomedical Imagin” by Hu Chen. So lets take a dive into their implementation and see what results we get. Finally, for fun let’s use different type of back propagation to compare … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 2, Convolutional Neural Network →

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 1, Vanilla Auto Encoder Model

From: https://towardsdatascience.com/only-numpy-medical-denosing-lung-ct-scans-using-auto-encoders-with-interactive-code-part-1-a6c3f9400246 Image from Pixel Bay My passion lies in Artificial Intelligent, and I want my legacy to be in the field of Health Care, using AI. So in hopes to make my dream come true as well as to practice OOP approach of implementing neural networks I will start the first part of long series … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 1, Vanilla Auto Encoder Model →

From: https://www.business-science.io/code-tools/2020/01/21/hyperparamater-tune-product-price-prediction.html Product price estimation and prediction is one of the skills I teach frequently - It's a great way to analyze competitor product information, your own company's product data, and develop key insights into which product features influence product prices. Learn how to model product car prices and calculate depreciation curves using the brand new tune package for Hyperparameter Tuning Machine Learning Models. This is … Continue reading Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial →

From: https://towardsdatascience.com/predicting-invasive-ductal-carcinoma-using-convolutional-neural-network-cnn-in-keras-debb429de9a6 In this blog, we will learn how to use CNN in a real world histopathology dataset. Real-world data requires a lot more preprocessing than standard datasets such as MNIST, and we will go through the process of making the data ready for classification and then use CNN to classify the images. I will … Continue reading Predicting Invasive Ductal Carcinoma using Convolutional Neural Network (CNN) in Keras →

From: https://machinelearningmastery.com/how-to-control-the-speed-and-stability-of-training-neural-networks-with-gradient-descent-batch-size/ Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated based on a subset of the training dataset. The number of examples from the training dataset used in the estimate of the error gradient is called the batch size and is an important hyperparameter that … Continue reading How to Control the Stability of Training Neural Networks With the Batch Size →

From: https://towardsdatascience.com/cortex-v0-13-8cf8c0f94edd Two years ago, my colleague and I came across TensorFlow, and despite having no idea what backpropagation or hidden layers were, we decided it would be cool to build machine learning applications. We found an abundance of resources for learning the basics of training machine learning models, but less information about deploying models … Continue reading Cortex v0.13: deploy machine learning models in production →

From: https://medium.com/swlh/how-data-augmentation-improves-your-cnn-performance-an-experiment-in-pytorch-and-torchvision-e5fb36d038fb Simple ways to boost your network performance Credits: https://amanispas.co.za/wp-content/uploads/2019/12/AdobeStock_241822083-Web-Crop-1360x680.jpg In simple terms, Data Augmentation is simply creating fake data. You use the data in the existing train set to create variations of it. This does two things — Increases the size of your training setRegularizes your network The book Deep Learningdefines regularization as any method … Continue reading How Data Augmentation Improves your CNN performance? — An Experiment in PyTorch and Torchvision →

From: http://www.sastibe.de/2020/01/sample_size_r/ A Standard Problem: Determining Sample Size Recently, I was tasked with a straightforward question: "In an A/B test setting, how many samples do I have to collect in order to obtain significant results?" As ususal in statistics, the answer is not quite as straightforward as the question, and it depends quite a bit … Continue reading Calculating the Required Sample Size for a Binomial Test in R →

From: https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db Fig. 1: The Inception module of InceptionTime. Index MotivationMachine Learning for Time Series ClassificationBest Deep Learning practices for Time Series Classification: InceptionTimeUnderstanding InceptionTimeConclusion 1. Motivation Time series data have always been of major interest to financial services, and now with the rise of real-time applications, other areas such as retail and programmatic advertising … Continue reading Deep Learning for Time Series Classification (InceptionTime) →

From: https://towardsdatascience.com/deep-portfolio-optimization-an-experiment-with-cryptocurrencies-106bf22a634 Feeling bullish. Image source Ifyou’ve ever taken an intro finance or investment theory class, then you’ve probably come across the idea of Modern Portfolio Theory or MPT. The Nobel Prize winning idea is that we have a collection of assets which have different returns, risks and correlations with each other, and we find an optimal weighing … Continue reading Deep Portfolio Optimization — An Experiment with Cryptocurrencies →