### Can Machine Learning Improve Recession Prediction?

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.

### Can Machine Learning Improve Recession Prediction?

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The code is documented and designed to be easy to extend. If you use it in your research, please consider referencing this repository.

### Can Machine Learning Improve Recession Prediction?

Big data utilization in economics and the financial world has increased with every passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance.1 This report outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy. Therefore, to help decision makers, we raise the question: Does machine learning and statistical data mining improve recession prediction accuracy?

In the previous post, Coding Neural Network - Forward Propagation and Backpropagation, we implemented both forward propagation and backpropagation in numpy. However, implementing backpropagation from scratch is usually more prune to bugs/errors. Therefore, it's necessary before running the neural network on training data to check if our implementation of backpropagation is correct. Before we start, let's revisit what back-propagation is: We loop over the nodes in reverse topological order starting at the final node to compute the derivative of the cost with respect to each edge's node tail. In other words, we compute the derivative of cost function with respect to all parameters, i.e $\frac{\partial J}{\partial \theta}$ where $\theta$ represents the parameters of the model.

### [D]What makes "Meta-SGD: Learning to Learn Quickly for Few-Shot Learning" to work so good? • r/MachineLearning

I'm interested in Few-Shot-Learning, so this paper is really intriguing for me either. I think that I still don't get paper (I'm not familiar with Meta-Learning), but learning algorithm look completely different than in normal supervised learning. So for weight update they use test set (which could be also a part of train set, not sure of proper name, but it would be better if we call it train-test and second one train-train). Do you see the difference? Why they use such idea?

### Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works

When I started to code neural networks, I ended up using what everyone else around me was using. But recently, PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it's dynamic computation graph paradigm. Don't worry if the last line doesn't make sense to you now. But take my word that it makes debugging neural networks way easier.

### Start With Gradient Boosting, Results from Comparing 13 Algorithms on 165 Datasets - Machine Learning Mastery

Which machine learning algorithm should you use? It is a central question in applied machine learning. In a recent paper by Randal Olson and others, they attempt to answer it and give you a guide for algorithms and parameters to try on your problem first, before spot checking a broader suite of algorithms. In this post, you will discover a study and findings from evaluating many machine learning algorithms across a large number of machine learning datasets and the recommendations made from this study. Start With Gradient Boosting, but Always Spot Check Algorithms and Configurations Photo by Ritesh Man Tamrakar, some rights reserved.

### Machine Learning Crash Course Google Developers

Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

### Implementing Variational Autoencoders in Keras: Beyond the Quickstart

It is a very well-designed library that clearly abides by its guiding principles of modularity and extensibility, enabling us to easily assemble powerful, complex models from primitive building blocks. This has been demonstrated in numerous blog posts and tutorials, in particular, the excellent tutorial on Building Autoencoders in Keras. As the name suggests, that tutorial provides examples of how to implement various kinds of autoencoders in Keras, including the variational autoencoder (VAE) [1]. Visualization of 2D manifold of MNIST digits (left) and the representation of digits in latent space colored according to their digit labels (right). Like all autoencoders, the variational autoencoder is primarily used for unsupervised learning of hidden representations.