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Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". The models are plotted and shown in the architecture sub folder. Due to lack of suitable training data (ILSVR 2015 dataset) and limited GPU processing power, the weights are not provided.

https://github.com/titu1994/Inception-v4Tags | inception-resnet paper keras deep-learning |

Implementation | Python |

License | MIT |

Platform | Windows Linux |

Simple Tensorflow implementation of "Squeeze and Excitation Networks" using Cifar10 (ResNeXt, Inception-v4, Inception-resnet-v2)

senet inception-resnet inception densenet resnext tensorflowA generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflowWelcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocationHigh level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

tensorflow zoo pretrained-models machine-learning deep-learning image-classification object-detection yolo yolov2 yolov3 faster-rcnn resnet inception nasnet pnasnet vgg densenet mobilenet mobilenetv2 squeezenetThis repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score on Cifar-10 dataset. Note that our codes are not faithful re-implementation of the original paper. This implementation has been tested with the following versions.

deep-learning generative-adversarial-network dcgan wgan-gpIn this work, we demonstrate a strong baseline two-stream ConvNet using ResNet-101. We use this baseline to thoroughly examine the use of both RNNs and Temporal-ConvNets for extracting spatiotemporal information. Building upon our experimental results, we then propose and investigate two different networks to further integrate spatiotemporal information: 1) temporal segment RNN and 2) Inception-style Temporal-ConvNet. Our analysis identifies specific limitations for each method that could form the basis of future work. Our experimental results on UCF101 and HMDB51 datasets achieve state-of-the-art performances, 94.1% and 69.0%, respectively, without requiring extensive temporal augmentation.

activity-recognition video-understanding torch lstm-neural-networks convolutional-neural-networkskeras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learningA simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. An accompanying tutorial can be found here. We also have implementations for GoBang and TicTacToe. To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. Example implementations for Othello can be found in othello/OthelloGame.py and othello/{pytorch,keras,tensorflow}/NNet.py.

tensorflow pytorch keras gobang gomoku alpha-zero alphago-zero alphago reinforcement-learning self-play mcts monte-carlo-tree-search othello tf deep-learning alphazeroDistributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

machine-learning deep-learning apache-spark data-parallelism distributed-optimizers keras optimization-algorithms tensorflow data-science hadoopThere are certainly a lot of guides to assist you build great deep learning (DL) setups on Linux or Mac OS (including with Tensorflow which, unfortunately, as of this posting, cannot be easily installed on Windows), but few care about building an efficient Windows 10-native setup. Most focus on running an Ubuntu VM hosted on Windows or using Docker, unnecessary - and ultimately sub-optimal - steps. We also found enough misguiding/deprecated information out there to make it worthwhile putting together a step-by-step guide for the latest stable versions of Keras, Tensorflow, CNTK, MXNet, and PyTorch. Used either together (e.g., Keras with Tensorflow backend), or independently -- PyTorch cannot be used as a Keras backend, TensorFlow can be used on its own -- they make for some of the most powerful deep learning python libraries to work natively on Windows.

theano gpu-acceleration deep-learning tensorflow cudnn cntk gpu-mode kerasThis is the code repository for Deep Learning with Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. This book starts by introducing you to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated Deep Convolutional Networks. In addition, you will also understand unsupervised learning algorithms such as Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks. Recurrent Networks and Long Short Term Memory (LSTM) networks are also explained in detail. You will also explore image processing involving the recognition of handwritten digital images, the classification of images into different categories, and advanced object recognition with related image annotations. An example of the identification of salient points for face detection is also provided.

This library is the official extension repository for the python deep learning library Keras. It contains additional layers, activations, loss functions, optimizers, etc. which are not yet available within Keras itself. All of these additional modules can be used in conjunction with core Keras models and modules. As the community contributions in Keras-Contrib are tested, used, validated, and their utility proven, they may be integrated into the Keras core repository. In the interest of keeping Keras succinct, clean, and powerfully simple, only the most useful contributions make it into Keras. This contribution repository is both the proving ground for new functionality, and the archive for functionality that (while useful) may not fit well into the Keras paradigm.

keras theano tensorflow machine-learning deep-learning neural-networks data-scienceProgrammatic representation of the Brilliant movie: INCEPTION by the Genius Director THY name is Christopher Nolan! My tribute to Nolan in "C" Language and a bit of assembly (x86) as the inception is done using x86 code morphing so that Fischer wakes up thinking that the thought was originated from his mind. Running the program would unravel the entire sequence in the movie. Reading the code would explain the movie Programmatically.

Inception is a physical memory manipulation and hacking tool exploiting PCI-based DMA. The tool can attack over FireWire, Thunderbolt, ExpressCard, PC Card and any other PCI/PCIe HW interfaces. Inception aims to provide a relatively quick, stable and easy way of performing intrusive and non-intrusive memory hacks against live computers using DMA.

Accelerating Deep Learning with Multiprocess Image Augmentation in Keras

deep-learning keras tensorflow multiprocessingSchematically, elephas works as follows. Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. For an introductory example, see the following iPython notebook.

spark keras neural-networks deep-learning distributed-computingJupyter notebooks for using & learning Keras

deep-learning keras-notebooks kerasDeep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearningHow simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

keras cnn cifar10 machine-learning tensorflow deep-learning neural-network imagenet image-processing nlpKeras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

deep-learning tensorflow theano neural-networks machine-learning data-science
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