/Simple-MLDL

Simple Machine Learning and Deep Learning projects.

Primary LanguageJupyter Notebook

Simple-MLDL

Simple Machine Learning and Deep Learning projects.

Environment:

Create the environment from the environment.yml file:

conda env create -f environment.yml

Logs:

Change the log_dir variable to the path where you want to store your logs.

List of Projects:

  • 1 - MNIST Handwritten Digit Recognition
Loss Accuracy Validation Loss Validation Accuracy
0.0173 0.9937 0.1005 0.9770
  • 2 - CIFAR-10 Image Classification
Loss Accuracy Validation Loss Validation Accuracy
0.8085 0.7308 0.9158 0.7292
  • 3 - Better CIFAR-10 Image Classification
Loss Accuracy Validation Loss Validation Accuracy
0.3765 0.8732 0.6425 0.8055
  • 4 - Cat and Dog Classification
Loss Accuracy Validation Loss Validation Accuracy
0.4120 0.8202 0.5702 0.7583

Kaggle Dataset: Dogs vs. Cats

  • 5 - Fruit Classification
Loss Accuracy Validation Loss Validation Accuracy
0.2321 0.9244 0.0951 0.9621

Kaggle Dataset: Fruits 360

  • 6 - Fashion MNIST Classification
Loss Accuracy Validation Loss Validation Accuracy
0.2488 0.9067 0.2623 0.9068
  • 7 - Monkey Breed Transfer Learning
Loss Accuracy Validation Loss Validation Accuracy
0.4898 0.8889 0.1708 0.9531

Kaggle Dataset: 10 Monkey Species

  • 8 - Flower Identification Transfer Learning
Loss Accuracy Validation Loss Validation Accuracy
1.0236 0.6761 0.4600 0.8504

Dataset: Flowers 17

  • 9 - Simpsons Character Classification
Loss Accuracy Validation Loss Validation Accuracy
0.3380 0.9099 0.1153 0.9604

Kaggle Dataset: The Simpsons Characters Data

  • 10 - Cat Binary Classifer - Logistic Regression from scratch
Loss Accuracy Validation Loss Validation Accuracy
- 81.5 - 76.0

Kaggle Dataset: Dogs vs. Cats

  • 11 - Linear Regression with TensorFlow 2.0 - Tutorial
Loss Accuracy Validation Loss Validation Accuracy
0.09 - - -
  • 12 - GAN for MNIST Handwritten Digits - Tutorial
Discriminator Loss Generator Loss Accuracy Real Accuracy Fake
0.688 0.732 0.21 0.92