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Page 1: Deep Learningcs230.stanford.edu/projects_winter_2019/posters/15401757.pdfHR m i n m a x [E/HR We use the perceptual loss function which is a weighted sum of a content loss (VGG loss)

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802990.pdf · 2019-04-04 · Both regression and classification approaches have been used to address issue of fake

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine learning model. For the audio classifier presented in this work, the inputs to the

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15809694.pdfexisting previous piece of artwork in a personalized manner. In our method, we alter an existing piece

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812470.pdf · upon them by pursuing deep learning techniques. Using techniques like LSTMs, RNNs, and highway networks,

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810428.pdf · Food image classification has many uses in everyday tasks, ranging from search to tagging. In our

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cs230.stanford.educs230.stanford.edu › projects_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813480.pdf · 2019-04-04 · Yog.ai: Anna Lai alai2@stanf ord.edu Deep Learning for Yoga Bhargav Reddy brkreddy@stanf

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813450.pdfInput (current flight) LSTM seq Dense (x5) Prediction Figure 2: LSTM + CNN Architecture Due to the class

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15871106.pdf5.0.2 Models Using the retrained Inception ResNet classifier, we ran several experiments to test the

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813330.pdf · According to the Federal Statistics Office, 2013, the number of newly opened insolvency proceedings

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