Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
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Glove As A Tensorflow Embedding LayerTaking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead.
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Lstm chemImplementation of the paper - Generative Recurrent Networks for De Novo Drug Design.
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IntrodatasciCourse materials for: Introduction to Data Science and Programming
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CaffeonsparkDistributed deep learning on Hadoop and Spark clusters.
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Python For Data ScientistsDeliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.
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Curso data scienceCódigo para el curso "Aprende Data Science y Machine Learning con Python"
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WotanAutomagically remove trends from time-series data
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Deprecated Boot CampsDEPRECATED: please see individual lesson repositories for current material.
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Airbnb Dynamic Pricing Optimization[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.
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Training MaterialA collection of code examples as well as presentations for training purposes
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Fcos tensorflowFCOS: Fully Convolutional One-Stage Object Detection.
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Knet.jlKoç University deep learning framework.
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Pascal Voc PythonRepository for reading Pascal VOC data in Python, rather than requiring MATLAB to read the XML files.
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GemfieldGemfield homework or libgemfield.so
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CaloganGenerative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation
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MobilenetssdfaceCaffe implementation of Mobilenet-SSD face detector (NCS compatible)
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Simple Qa Emnlp 2018Code for my EMNLP 2018 paper "SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach"
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Book Mlearn GyomuBook sample (AI Machine-learning Deep-learning)
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Ml Cv机器学习实战
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Few Shot Text ClassificationCode for reproducing the results from the paper Few Shot Text Classification with a Human in the Loop
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Stanford Project Predicting Stock Prices Using A Lstm NetworkStanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
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Zh Nlp Demo自然语言处理NLP在中文文本上的一些应用,如文本分类、情感分析、命名实体识别等
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Sphinx Book ThemeA lightweight book theme built off of the pydata sphinx theme
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GanspaceDiscovering Interpretable GAN Controls [NeurIPS 2020]
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Detection Hackathon Apt29Place for resources used during the Mordor Detection hackathon event featuring APT29 ATT&CK evals datasets
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Aureliengeron“Hands-On Machine Learning with Scikit-Learn and TensorFlow” Excerpt From: Aurélien Géron. “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.” iBooks.
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Game Theory And PythonGame Theory and Python, a workshop investigating repeated games using the prisoner's dilemma
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ConvgpConvolutional Gaussian processes based on GPflow.
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Text objsegCode release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016
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Local light field synthesisLocal Light Field Synthesis (Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng ICCV 2017)
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Spark Nlp ModelsModels and Pipelines for the Spark NLP library
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Viz torch optimVideos of deep learning optimizers moving on 3D problem-landscapes
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MagnetMAGNet: Multi-agents control using Graph Neural Networks
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Smiles TransformerOriginal implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.
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SamplevaeMulti-purpose tool for sound design and music production implemented in TensorFlow.
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PytorchPyTorch tutorials A to Z
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