Log.md

Day 1: 7^th July 2018, Saturday

Today's Progress:

• Brushed up the basics of Linear Algebra.
• Implemented Linear Regression with one variable using Gradient Descent referring Andrew Ng's Stanford ML course.

Link of Work Link

Day 2: 8^th July 2018, Sunday

Today's Progress:

• Implemented Linear Regression with multiple variables using Gradient Descent and Normal equation.

Link of Work Link

Day 3: 9^th July 2018, Monday

Today's Progress:

• Was facing trouble in understanding vectorized implementations using numpy arrays, so referred to various blogs and videos and rewrote few functions to their fully vectorized form.

Day 4: 10^th July 2018, Tuesday

Today's Progress:

• Went through the theory of Logistic Regression for two class classification problem and learnt things regarding:
  • Logit function and Logistic Regression Model
  • Hypothesis Representation
  • Linear and Non-Linear decision boundaries

Day 5: 11^th July 2018, Wednesday

Today's Progress:

• Went through the theory of Logistic Regression for two class classification problem and learnt things regarding:
  • Logistic Regression Cost function
  • Gradient descent for optimizing the cost function

Day 6: 12^th July 2018, Thursday

Today's Progress:

• Learnt about different advanced optimization algorithms(e.g Conjugate gradient, BFGS),which are better than gradient descent.
• Understood vectorized implementation of Logistic Regression.
• Went through the theory of Multi-Class Logistic Regression for classification.