Neural Networks and Deep Learning  

This course covered the foundations of deep learning including topics like:

  • Understand the major technology trends driving Deep Learning
  • Be able to build, train and apply fully connected deep neural networks
  • Know how to implement efficient (vectorized) neural networks
  • Understand the key parameters in a neural network’s architecture

Algorithmic Toolbox  

This course covered basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming.

R Programming  

In this course I learned how to program in R and how to use R for effective data analysis. This course covered practical issues in statistical computing which included programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

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