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Deep_Neural_Networks_From_Scratch

Timeline and files in implementing DNN from scratch

File Description

  • 00- v1.0 - Data preparation file
  • 01- v1.1 - Logistic Regression and 2 Layer Shallow Neural Network (sigmoid -> sigmoid)
  • 02- Data augmentation (increasing dataset, thus creating new dataset)
  • 03- v1.1.1 - v1.1 with new dataset
  • 04- v1.2 - shallow nn implementation (relu -> sigmoid) on original dataset
  • 05- v1.2.1 - v1.2 with new dataset (relu -> sigmoid)
  • 06- v1.3 - deep nn with original dataset ((n-1)relu -> sigmoid)
  • 07- v1.3.1 - deep nn with new dataset
  • 08- v1.3.2 - v1.3.1 with He/Xavier initialization and Dropout regularization (failed)
  • 09- v1.3.3 - v1.3.1 with He/Xavier Initialization
  • 10- v1.4 - DNN with He/Xavier initialization and Adam optimizer
  • 11- v_1.1 - 3 classes instead of 2
  • 12- v_1.1.1 - softmax function changed
  • 13- v_1.2 - uses adam optimizer
  • 14- v_1.2.1 - now 7 classes,with adam optimizer

File No. Training Accuracy Testing Accuracy
01 (logistic) 100 % 73.77 %
01 (shallow) 100 % 78 %
03 (logistic) 100 % 68.59 %
03 (shallow) 100 % 67 %
04 100 % 75.41 %
05 99.99 % 81.81 %
06 100 % 73.77 %
07 99.9 % 74.38 %
08 (for 2 layers) 98.76 % 76.86 %
08 (for 4 layers,failed) -- --
09 99.22 % 74.38 %
10 99.05 % 79.12 %
11 38.9 % 35.64 %
12 95.52 % 61.69 %
13 64.63 % 53.23 %
14 100 % 32.11 %

Dataset from - Dog Dataset

Code Snippets from

Course1

Course2

Master Course

Inspired by - Andrew Ng

Project partner- Rahul Lamge

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Timeline and files in implementing DNN from scratch

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