Transformer TtsImplementation of "FastSpeech: Fast, Robust and Controllable Text to Speech"
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Tianchi ship 2019天池智慧海洋 2019 https://tianchi.aliyun.com/competition/entrance/231768/introduction?spm=5176.12281949.1003.1.493e5cfde2Jbke
Stars: ✭ 54 (-1.82%)
CommitgenCode and data for the paper "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes"
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25daysinmachinelearningI will update this repository to learn Machine learning with python with statistics content and materials
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TelepythTelegram notification with IPython magics.
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Figure GenA Python package to effortlessly assemble images in comparison figures. Supports LaTeX, PPTX, and HTML.
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Pyspark Setup GuideA guide for setting up Spark + PySpark under Ubuntu linux
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Mypresentationsthis is my presentaion area .个人演讲稿展示区,主要展示一些平时的个人演讲稿或者心得之类的,
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Sklearn DeeprlDeep reinforcement learning. In scikit-learn. In less than 50 effective lines.
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ComettsComet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons
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Handwritten Character RecognitionThis a Deep learning AI system which recognize handwritten characters, Here I use chars74k data-set for training the model
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Keras2kubernetesOpen source project to deploy Keras Deep Learning models packaged as Docker containers on Kubernetes.
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DanetDeep Attractor Network (DANet) for single-channel speech separation
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BrihaspatiCollection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ✨💥
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365datascience This Repo Contains all the exercise files for Data Science Course of 365 Datascience . The repo is split into the relevant folders & there is one exercise folder which contains all the files of that course. Don't forget to star it :D
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TrdesigntrRosetta for protein design
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Deep3dAutomatic 2D-to-3D Video Conversion with CNNs
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Info490 Sp17Advanced Data Science, University of Illinois Spring 2017
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Stock Market Prediction Using Natural Language ProcessingWe used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.
Stars: ✭ 53 (-3.64%)
Github PaperPlos in Computational Biology paper related with github for researchers, code, source and document
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Style Transfer ColabGoogle Colab Notebook for Image and Video Style Transfer Using TensorFlow
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Ds Python Data AnalysisData manipulation, analysis and visualisation in Python - specialist course Doctoral schools of Ghent University
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Policy Gradient MethodsImplementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
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Bts PytorchPyTorch implementation of BTS Depth Estimator
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Ganpython notebooks accompanying the book Make Your Own GAN
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Ipython NotebooksSome iPython Notebooks I have created for personal learning
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Facenet Face RecognitionThis is the research product of the thesis manifold Learning of Latent Space Vectors in GAN for Image Synthesis. This has an application to the research, name a facial recognition system. The application was developed by consulting the FaceNet model.
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Neural Process FamilyCode for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
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BlogRead and Write
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TensorflowTensorflow实战学习笔记、代码、机器学习进阶系列
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Average Word2vec🔤 Calculate average word embeddings (word2vec) from documents for transfer learning
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MishOfficial Repsoitory for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
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Info490 Fa16INFO 490: Foundations of Data Science, offered in the Fall 2016 Semester at the University of Illinois
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WhitehatInformation about my experiences on ethical hacking 💀
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ModernaiMaterials for Modern AI Course / Cloud Day 2.0
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Tutoriais De AmAlgoritmos de aprendizado de máquina criados manualmente para maior compreensão das suas funcionalidades
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