/ML_Recruitment

Primary LanguageJupyter Notebook

Introduction to Machine Learning Terminologies

Learn about the following terminologies:

  • Accuracy, Precision and F-Score
  • Curse of Dimensionality
  • Bias Variance Tradoff
  • Loss Functions

Resources

Wikpedia Articles

Genetic Algorithms

What are Genetic Algorithms?

Task 1

Merge this repository into your fork/clone

Github Merging into fork

Solve GA_task.ipynb notebook

Implement a GA to extremize a polynomial within range.

Neural Networks

Visualization:

Youtube videos - good, intuitive, in-depth https://youtu.be/aircAruvnKk

Online e-book

http://neuralnetworksanddeeplearning.com/

Neural network made by google - Can understand hyper-parameter tuning

Tensorflow Playground

Task 2

Solve fill and solve neural_net.py file

Resources:

Instructions:

There are 11 TODOS in this python file Fill each one of those appropriately and you will have a working neural network Instructions and resources have been provided wherever possible. The implementation may not be perfect, so feel free to point out any mistakes / ask any doubts

After completing the task, some of the things you could try are (optional):

  • Implement different cost functions (binary cross-entropy)
  • Implement different activation functions (tanh, ReLU, softmax)
  • Incorporate these changes in the neural netwok code so that you can select the loss / activation function
  • Play with the hyper-paramters!

Genetic Algorithms and Neural Networks

Combining Genetic algorithms and neural networks Read up on these resources as. Later we will ask you to implement something similar.