marktechpost

A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then ...
The Earth Institute Columbia University

Wasserstein Generative Adversarial Networks (WGANs) Part 1: An Introduction

Since its inception in 2014 by Goodfellow et al, generative adversarial networks (GANs) have taken the research community by storm. The business for improving GANs has grown to a point where papers on ...
GitHub

Very low test accuracy on mnist tutorial

Hello, I am following the mnist tutorial but am unable to get ~99% accuracy even after 12 epochs. I am running the latest version of keras (2.0.7) with tensorflow ...
InfoWorld

MXNet review: Amazon’s scalable deep learning

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep ...
InfoWorld

Review: TensorFlow shines a light on deep learning

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs What makes Google Google? Arguably it is machine ...
GitHub

MNIST_Dataset_and_Databases.ipynb

"In the [MNIST tutorial](https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/MNIST.ipynb) we use an lmdb database. You can also use leveldb or even ...