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This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

deep-learning pytorch-tutorial neural-networks pytorch tutorial tensorboardPyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch pytorch-tutorials pytorch-tutorials-cn deep-learning neural-style charrnn gan caption neuraltalk image-classification visdom tensorboard nn tensor autograd jupyter-notebookThis chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odeFor a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.

tensorflow object-detection faster-rcnn coco voc resnet mobilenet tensorboardThis repository allows you to get started with training a State-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it with TensorBoard. You can even test your model with our built-in Inference REST API. Training with TensorFlow has never been so easy.

docker gui deep-neural-networks computer-vision deep-learning neural-network tensorflow rest-api tensorboard resnet deeplearning object-detection nvidia-docker computervision objectdetection no-code tensorflow-training detection-api tensorflow-gui inference-apiCrayon is a framework that gives you access to the visualisation power of TensorBoard with any language. Currently it provides a Python and a Lua interface, however you can easily implement a wrapper around the provided RESTful API. Note that the server and the client do not have to be on the same machine.

crayon tensorboard tensorflow pytorch torch7 deep-learning data-visualization dockerFigure 1: Original image and the reconstructed versions from maxpool layer 1,2 and 3 of Alexnet generated using tf_cnnvis. The function to generate the activation visualizations of the input image at the given layer.

tensorflow tensorboard convolutional-neural-networks cnn visualization deepdream convolutional-networksThis repository, based on AlexeyAB's darknet repro, allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv4 has never been so easy. This repository has also cross compatibility with Yolov3 training.

docker gui automation monitoring deep-learning neural-network rest-api yolo tensorboard deeplearning object-detection darknet computervision objectdetection no-code yolov3 alexeyab-darknet yolo-gui yolo-tensorboard yolov4The What-If Tool (WIT) provides an easy-to-use interface for expanding understanding of a black-box classification or regression ML model. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programmatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset. The purpose of the tool is that give people a simple, intuitive, and powerful way to play with a trained ML model on a set of data through a visual interface with absolutely no code required.

visualization machine-learning tensorboard jupyterlab-extension colaboratory ml-fairness mlTensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and the graph.This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.

tensorboard visualization mxnetWhile research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experimentsDeepScite takes in papers (titles, abstracts) and emits recommendations on whether or not they should be scited by the particular users whose data we've used for training (in the case of this repo, it is me). As output, it also gives a "goodness" score for each word; when this number is high, it has contributed strongly to the paper being (recommended) for sciting, when it is negative, it has contributed strongly to the paper not being recommended.

deep-learning embeddings convolution tensorboard tensorflowThis is a PyTorch implementation of the DenseNet architecture as described in Densely Connected Convolutional Networks by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. This implementation currently supports training on the CIFAR-10 and CIFAR-100 datasets (support for ImageNet coming soon).

pytorch densenet tensorboardtfgraphviz is a module to visualize a TensorFlow's data flow graph like TensorBoard using Graphviz. tfgraphviz enables to provide a visualization of tensorflow graph on Jupyter Notebook without TensorBoard. The only dependency is Graphviz.

tensorflow tensorboard machine-learning visualization graphviz deep-learning neural-network dataflow-programmingWrite tensorboard events with simple command. including scalar, image, histogram, audio, text, graph and embedding. This is based on tensorboard-pytorch.

chainer tensorflow tensorboardA hacked-up visualization tool for caffe2. Specifically, it dumps the computation graph and the training statistics of caffe2 into a tensorboard compatible format. Once it starts dumping, you can use tensorboard to visualize the results. These screen shots are taken when training a detector with detectron.

caffe2 tensorboard visualization deep-learning neural-networks artificial-intelligenceThis is a random collection of utilities for inspecting TensorFlow summary files.

tensorflow tensorboardA TensorFlow utility for providing matplotlib-based plot operations — TensorBoard ❤️ Matplotlib. It allows us to draw any matplotlib plots or figures into images, as a part of TensorFlow computation graph. Especially, we can easily any plot and see the result image as an image summary in TensorBoard.

tensorflow tensorboard matplotlib plot tfplotIn this repository, source codes will be shared while capturing "TensorFlow 101: Introduction to Deep Learning" online course published on Udemy. The course consists of 18 lectures and includes 3 hours material.

tensorflow tensorboard dnn neural-networks deep-learning deep-neural-networks classification regression clustering k-means kmeans supervised-learning unsupervised-learning machine-learning python-3TL;DR: pip install dytb + python-notebook with a complete example. DyTB comes with some common ML model, like LeNet & VGG, if you want to test how these models perform when trained on different datasets and/or with different hyperparameters, just use it.

tensorflow training tensorboard neural-network convolutional-neural-networks dataset models
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