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Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

mnist deep-learning benchmark machine-learning dataset computer-vision fashion fashion-mnist gan zalando convolutional-neural-networks텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inceptionGPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

regl cnn digit-recognition demo gpu webgl convolutional-neural-networks gpgpu deep-learning glsl digit recognition mnist convolutional neural network networksAll pull requests are welcome, make sure to follow the contribution guidelines when you submit pull request.

tensorflow tensorflow-tutorials mnist-classification mnist machine-learning android tensorflow-models machine-learning-android tensorflow-android tensorflow-model mnist-model deep-learning deep-neural-networks deeplearning deep-learning-tutorialMy implementations of deep neural networks for practice.

deep-learning deep-neural-networks generative-adversarial-network mnist jupyter-notebook wasserstein-gan dragan discoganTensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

cifar-10 generative-adversarial-networks mnist lenet-5 convolutional-neural-networks resnet-18 notebook deep-learning tensorflow resnet tfrecord-format cifar-100 tensorflow-tutorials kmeans-clustering perceptron autoencoder tutorialA directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

machine-learning dropout lstm mnist lenet neural-turing-machines question-answering computational-graphs auto-differentiation convolutional-neural-networks convolutional-networks recurrent-neural-networks lstm-model deep-learning deep-q-network reinforcement-learning cartpoleThis is an educational effort to help understand how deep neural networks work. In order to achieve this goal I prepared a small number of selected educational materials and heavily documented pure C++ implementation of CNN that classifies MNIST digits.

convolutional-neural-networks deep-learning cpp from-scratch cnn mnist neural-network computer-visionThere are six snippets of code that made deep learning what it is today. Coding the History of Deep Learning on Floydhub' s blog covers the inventors and the background to their breakthroughs. In this repo, you can find all the code samples from the story.

deep-learning linear-regression mnist perceptron least-squares gradient-descent backpropagationThis is an implementation of the paper Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks https://arxiv.org/abs/1710.10393. Label Embedding Network can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned through back propagation. The original one-hot represented loss function is converted into a new loss function with soft distributions, such that the originally unrelated labels have continuous interactions with each other during the training process. As a result, the trained model can achieve substantially higher accuracy and with faster convergence speed. Experimental results based on competitive tasks demonstrate the effectiveness of the proposed method, and the learned label embedding is reasonable and interpretable. The proposed method achieves comparable or even better results than the state-of-the-art systems.

label-embedding deep-learning label-representation cifar10 cifar100 mnist computer-vision natural-language-processingThe original concept of this notebook was based on a Machine Learning (intern) candidate tech challenge from the Toronto startup 500px. When I first saw the posting, it was at the beginning of my 3 month career pivot into Deep Learning and I thought this challenge would be a great way for me to benchmark my progress once I get started. You can read more about my career transition journey on Medium and a revised/updated version on LinkedIn. Although, I didn't follow through with providing the entire final output of the challenge, I'm quite satisfied that I've successfully completed it and consider it a demonstration of my current knowledge and capability. Prior to starting this challenge, I completed Fast.ai: Practical Deep Learning - Part 1. Read through my blog post to see my reading material - Deep Learning Reading List.

tensorflow mnist deep-learning adversarial 500px
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