{"id":21269737,"url":"https://github.com/richardwarepam16/be_dl_practicals","last_synced_at":"2026-05-20T06:10:09.861Z","repository":{"id":164351238,"uuid":"637668169","full_name":"richardwarepam16/BE_DL_Practicals","owner":"richardwarepam16","description":"Deep Learning Practicals of B.E. 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Use Boston House price predictiondataset.\n[Code given by College](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab1.ipynb)\n\n[Click here to view the Jupyter Notebook: My Code](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab1_1.ipynb)\n\n#### Lab 2: Classification using Deep neural network: Binary classification using Deep Neural Networks Example: Classify movie reviews into positive\" reviews and \"negative\" reviews, just based on the text content of the reviews. Use IMDB dataset\n[Click here to view the Jupyter Notebook](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab2.ipynb)\n\n#### Lab 3: Convolutional neural network (CNN): Use MNIST Fashion Dataset and create a classifier to classify fashion clothing into categories.\n[Code given by College](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab3.ipynb)\n\n[Click here to view the Jupyter Notebook: My Code](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab3_1.ipynb)\n\n#### Lab 4: Recurrent neural network (RNN) Use the Google stock prices dataset and design a time seriesanalysis and prediction system using RNN.\n[Click here to view the Jupyter Notebook: My Code](https://github.com/richardwarepam16/BE_DL_Practicals/blob/master/lab4.ipynb)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frichardwarepam16%2Fbe_dl_practicals","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frichardwarepam16%2Fbe_dl_practicals","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frichardwarepam16%2Fbe_dl_practicals/lists"}