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PseudoLab Tutorial Book

  • Deep Learning Tutorials with PyTorch

Object Detection

  • Detecting Medical Masks
  • 1. Introduction to Object Detection
  • 2. EDA
  • 3. Data Preprocessing
  • 4. RetinaNet
  • 5. Faster R-CNN
  • 6. References

Time Series

  • Predicting Confirmed Cases of Covid-19
  • 1. Introduction to Time Series
  • 2. EDA
  • 3. Data Pre-Processing
  • 4. LSTM
  • 5. CNN-LSTM
  • 6. References
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Contents
  • Datasets
  • Papers
  • Code
  • Blog Posts

6. References¶

Datasets¶

  • JHU CSSE COVID-19 Data

  • Novel Corona Virus 2019 Dataset

Papers¶

  • Modeling COVID-19 Scenarios for the United States

  • Time Series Forecasting With Deep Learning: A Survey

  • Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

  • ANOTHER LOOK AT FORECAST-ACCURACY METRICS FOR INTERMITTENT DEMAND

  • Accuracy measures: theoretical and practical concerns

  • Another look at measures of forecast accuracy

Code¶

  • Covid-19 USA Visualization & Forecasting

  • Covid-19 Forecasting with an RNN

  • covid-19 forecast Germany with lgbm and keras

  • Predicting COVID-19 Infections with LSTM

  • Time Series Forecasting with LSTMs for Daily Coronavirus Cases using PyTorch in Python

  • Time Series Prediction using LSTM with PyTorch in Python

  • Forecasting: Principles and Practice

  • Deep Learning for Time Series Forecasting

  • SEQUENCE MODELS AND LONG-SHORT TERM MEMORY NETWORKS

Blog Posts¶

  • Recurrent Neural Networks (RNNs)

  • LSTM hidden state logic

  • Understanding 1D and 3D Convolution Neural Network | Keras

5. CNN-LSTM

By PseudoLab Tutorial Team
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