Computer Learns To Create Its Own Pokémon, And They're Great.
CIFAR-10 and CIFAR-100 datasets. < Back to Alex Krizhevsky's home page The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset.
They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. Here are the classes in the dataset, as well as 10 random images from each: The classes are completely mutually exclusive. Download If you're going to use this dataset, please cite the tech report at the bottom of this page. Baseline results You can find some baseline replicable results on this dataset on the project page for cuda-convnet. Other results Rodrigo Benenson has been kind enough to collect results on CIFAR-10/100 and other datasets on his website; click here to view. Dataset layout Python / Matlab versions Binary version.
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks. Recognizing Objects with Deep Learning You might have seen this famous xkcd comic before.
The goof is based on the idea that any 3-year-old child can recognize a photo of a bird, but figuring out how to make a computer recognize objects has puzzled the very best computer scientists for over 50 years. In the last few years, we’ve finally found a good approach to object recognition using deep convolutional neural networks. That sounds like a a bunch of made up words from a William Gibson Sci-Fi novel, but the ideas are totally understandable if you break them down one by one.
So let’s do it — let’s write a program that can recognize birds! Starting Simple Before we learn how to recognize pictures of birds, let’s learn how to recognize something much simpler — the handwritten number “8”. In Part 2, we learned about how neural networks can solve complex problems by chaining together lots of simple neurons. Datasets for Data Mining and Data Science. See also Data repositories AssetMacro, historical data of Macroeconomic Indicators and Market Data.
Awesome Public Datasets on github, curated by caesar0301. AWS (Amazon Web Services) Public Data Sets, provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. Ssathya/IMDBMongo: Process files from IMDB ... Installation - TFLearn. Tensorflow Installation TFLearn requires Tensorflow (version >= 0.9.0) to be installed.
Select the correct binary to install, according to your system: # Ubuntu/Linux 64-bit, CPU only, Python 2.7 $ export TF_BINARY_URL= # Ubuntu/Linux 64-bit, GPU enabled, Python 2.7# Requires CUDA toolkit 7.5 and CuDNN v5. Numpy.ndarray — NumPy v1.12 Manual. Scipy.ndimage.imread — SciPy v0.14.0 Reference Guide. Download and Setup You can install TensorFlow either from our provided binary packages or from the github source.
Requirements The TensorFlow Python API supports Python 2.7 and Python 3.3+. The GPU version works best with Cuda Toolkit 8.0 and cuDNN v5.1. Other versions are supported (Cuda toolkit >= 7.0 and cuDNN >= v3) only when installing from sources. Please see Cuda installation for details. Overview We support different ways to install TensorFlow: Pip install: Install TensorFlow on your machine, possibly upgrading previously installed Python packages. If you are familiar with Pip, Virtualenv, Anaconda, or Docker, please feel free to adapt the instructions to your particular needs. If you encounter installation errors, see common problems for some solutions.
Pip installation The packages that will be installed or upgraded during the pip install are listed in the REQUIRED_PACKAGES section of setup.py. Install pip (or pip3 for python3) if it is not already installed: Neural networks and deep learning. In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks.
That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow nets. But while the news from the last chapter is discouraging, we won't let it stop us. In this chapter, we'll develop techniques which can be used to train deep networks, and apply them in practice. We'll also look at the broader picture, briefly reviewing recent progress on using deep nets for image recognition, speech recognition, and other applications.
And we'll take a brief, speculative look at what the future may hold for neural nets, and for artificial intelligence. The chapter is a long one. The main part of the chapter is an introduction to one of the most widely used types of deep network: deep convolutional networks. Many of these are tough even for a human to classify. It's worth noting what the chapter is not. Problem Exercise.