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Sixth Competition following Youhan Lee's curriculum. Multi-class classification competition using image data. TensorFlow Speech Recognition ChallengeCan you build an algorithm that understands simple speech commands?www.kaggle.comFirst Kernel: Speech representation and data explorationMainly about exploring speech data.Insight / Summary:1. VisualizationThere are two theories of a human hearing -..

This post heavily relies on Will Koehrsen's public codes on Home Credit Default Risk Competition. https://www.kaggle.com/code/willkoehrsen/start-here-a-gentle-introductionhttps://www.kaggle.com/code/willkoehrsen/introduction-to-manual-feature-engineering  Above 2 posts are the first 2 public codes(kernels) on this competition written by Koehrsen and they are reviewed from my previous post:  [Kag..

Fourth Competition following Yuhan Lee's curriculum. Binary classification competition using image data. Statoil/C-CORE Iceberg Classifier ChallengeShip or iceberg, can you decide from space?www.kaggle.comFirst Kernel: Keras Model for Beginners (0.210 on LB)+EDA+R&DEDA + basic modelingUsing Keras which makes building deep neural networks very easy.Basic conv net model.Insights/Summary:1. Calcula..

Third Competition following Yuhan Lee's kaggle curriculum. Binary classification competition using tabular data. First KernelGeneral EDA kernel. Insights/Summary:1. This kernel's eda was processed by this order:target column(binary var) analysisMissing values examinationdata type checkingunique classes checking for object(categorical) columnsencoding categorical columnsanomalies detectioncheckin..

There are several types of correlation analysis:Pearson CorrelationMost commonly used methodMeasures linear relationshipsSuitable for continuous variablesValues range from -1 to 1Assumes normal distributionSpearman CorrelationMeasures rank-based correlationCaptures non-linear relationships wellSuitable for ordinal dataLess sensitive to outliersCan be used even when data is not normally distribut..

Skip Connection?Skip connections are now a standard module in many convolutional architectures.They provide alternative paths for gradients (including backpropagation). This additional pathway has been experimentally proven to often help with model convergence.In deep architectures, skip connections, as the name suggests, skip some layers of the neural network and provide the output of one layer..

Translation InvarianceTranslation invariance in CNN means that even if the input position changes, the output maintains the same value.Actually, CNN networks themselves have translation equivariance (variance).Meaning that, when computing with convolution filters, if the position of a specific feature changes, the position of that feature's computation result in the output naturally changes as w..

This post heavily relies on Andrew Ng's lecture: 컨볼루션 신경망DeepLearning.AI에서 제공합니다. 딥러닝 전문 과정의 네 번째 과정에서는 컴퓨터 비전이 어떻게 발전해 왔는지 이해하고 자율주행, 얼굴 인식, 방사선 이미지 판독 등 흥미로운 응용 분야에 익숙해www.coursera.orgAdvantages of Convolutional Layers Over Fully Connected LayersFollowing above example, if we use Fully-connected layer instead of Convolutional layer, we should connect 3,072 and 4,704 neurons.That is, the number..

Second competition following Yuhan Lee's kaggle curriculum. Binary classification competition using tabular data.First KernelData preparation and exploration kernel.Insights / Summary:1.info method (e.g. train.info()) provides data type, null value existence, number of rows of each variable of the dataframe.2. Storing metadata for each variable might help data management.Code example:data = []fo..

This post heavily relies on the book 'Natural Language Processing with Pytorch':https://books.google.co.kr/books?id=AIgxEAAAQBAJ&printsec=copyright&redir_esc=y#v=onepage&q&f=falseNLP?A set of techniques that solve practical problems using statistical methods to understand text, regardless of linguistic knowledge.The 'understanding' of text is mainly achieved by converting text into computable re..