Machine Learning and Deep Learning

My Work

• Handwritten Digits Classification Using Deep Neural Network
  • Implemented Deep Neural Network from previous project Cat Classification Using Deep Neural Network
  • Modified Input and Output layer size
  • Used MNIST dataset
  • Added Feature to Save trained model and retrain same model
  • Used modules Numpy, Matplotlib, Scipy, PIL, pickle, Skimage
• Cat Classification Using Deep Neural Network
  • Implemented Neural Network with Multiple Layers
  • Added feature for saving our trained model
  • Can predict from images provided from user
  • Created by doing some modification in deeplearning.ai assignment
  • Used modules Numpy, Scipy, Matplotlib, PIL, pickle
• Classification with Shallow Neural Network
  • Implemented Neural network with one Hidden Layer
  • Trained using dataset from sklearn
  • Compared Accuracy against different Datasets, No. of Hidden Layer Neurons
  • Used modules Sklearn, Matplotlib, Numpy
• Logistic Regression - Cat Classification
  • Made by keeping neural network in mind.
  • Compared accuracy for different Learning Rate and No of Iterations
  • created by doing some modification in my Deeplearning.ai programming assignment
  • Used modules Numpy, Matplotlib, Scipy, Skimage, Pickle, PIL
• Linear Regression with Tensorflow
  • Used Tensorflow
  • Used Gradient Descent for optimization
  • Random Dataset created using Numpy
  • Used modules Tensorflow,Numpy and Matplotlib.

Here I'll be posting my work as I learn and make.

Resources from where I am learning

1. Machine Learning by Stanford University- Coursera
2. Deeplearnin.ai Specialization - Coursera
   • Neural Network and Deeplearning
   • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
   • Structuring Machine Learning Projects
   • Convolutional Neural Networks
   • Sequence Models
3. Tensorflow Documentation
4. Scipy and Numpy Documentation
5. Matplotlib
6. Udacity