Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine...

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Transcript of Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine...

Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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
Page 7: 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

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

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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15792433.pdf · supermarkets: four Safeways near Palo Alto, SF and one H-E-B in Austin, TX. Between 40 and 60 snapshots

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15792433.pdf · supermarkets: four Safeways near Palo Alto, SF and one H-E-B in Austin, TX. Between 40 and 60 snapshots

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated

AIR CLASSIFIER TURBO CLASSIFIER - AAAmachine

AIR CLASSIFIER TURBO CLASSIFIER - AAAmachine

Enhanced Smoothing Methods Using Na ve Bayes Classifier ... · III Naïve Bayes classifier A Naive Bayes classifier is a simple probabilistic classifier which is based on applying

Enhanced Smoothing Methods Using Na ve Bayes Classifier ... · III Naïve Bayes classifier A Naive Bayes classifier is a simple probabilistic classifier which is based on applying

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

Grit Classifier

Grit Classifier

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

Dynamic Classifier Systems for Classifier Aggregation

Dynamic Classifier Systems for Classifier Aggregation

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939642.pdf · with a non-native clip as the "content" and a US accent clip as the "style". The CNN classifier was

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939642.pdf · with a non-native clip as the "content" and a US accent clip as the "style". The CNN classifier was

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

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 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,

SVM classifier

SVM classifier

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long.

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long.

The Naïve Bayes Classifier - svivek · •The naïve Bayes Classifier •Learning the naïve Bayes Classifier •Practical concerns 2. Today’s lecture •The naïve Bayes Classifier

The Naïve Bayes Classifier - svivek · •The naïve Bayes Classifier •Learning the naïve Bayes Classifier •Practical concerns 2. Today’s lecture •The naïve Bayes Classifier

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813327.pdf0.024 0.022 0.020 Train 0.029 0.036 0.051 Test 0.048 0.026 0.028 Train 81.19% 87.55% 74.60% Test 85.82%

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813327.pdf0.024 0.022 0.020 Train 0.029 0.036 0.051 Test 0.048 0.026 0.028 Train 81.19% 87.55% 74.60% Test 85.82%

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 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 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational