This is the first tutorial of this series, which covers the basic concepts of Kafka
In this blog, we will discuss an interview question asked by Google.
In this post, we are going to discuss the leetcode problem no. 1055 — Shortest Way to Form String, which is recently asked in Google interviews.
Firstly, we will try to solve the problem with brute force method in O(n²) running time; then we will improve our algorithm to O(n log n) running time; finally we will further improve the algorithm to O(n).
The input of the problem is two strings:
Recent interview question asked by Google and Amazon.
In this post, we are going to discuss the leetcode problem no. 253 — Meeting Rooms II. This problem is recently asked in Google and Amazon interviews.
The input of the problem is a list of meeting schedules. Each meeting schedule is represented by a list of two items, with the first item as the starting time of a meeting, and the second item as the ending time of the meeting.
The expected output of the problem is an integer, which is the minimum number of meeting rooms required to fulfill the meeting schedules. …
This is an introduction to dynamic array and its implementation
An array is a contiguous area of memory of equal-size elements. Array length is usually fixed, which means you need to specify the number of elements your array can hold ahead of time.
A dynamic array is an array which has an important feature: auto-resizing. With this feature, you can easily expand your array by adding more elements in it, so that you do not need to determine the size ahead of time.
In this tutorial, we will discuss about the implementation of dynamic array by using regular fixed length array. …
Cheat sheet on numpy
By using built-in numpy functions such as `np.dot` will eliminate the need of a `for loop`. It enables Phyton Pi to take much better advantage of parallelism to do computations on arrays much faster.
In this post, I will discuss details about Gradient Descent and Back-propagation in neural networks, and will help you to understand why there is Vanishing Gradient Problem. In my earlier post (How to implement Gradient Descent in Python), I discussed python implementation of Gradient Descent. In this post, I will show you step-by-step to understand the math behind the Neural Networks.
For the Neural Networks discussed in the post, we will make the following assumptions:
Gradient Descent is the most important concept in Neural Networks. In this tutorial, I am going to show you how to implement it in Python. I hope this tutorial can help you to build a better understanding about how gradient descent works, and how it helps to improve model accuracy.
We will try to build a single neuron network, which can predict the admissions of a graduate school. The data we will use is shared above in google drive. Let us first take a peek at the raw data:
The first 5 rows of data are shown below. The first column
admit indicates whether the student is getting admitted to the school or not, this will be the target for our model; the second column
gre and the third column
gpa are numerical features for the student; the fourth column
rank is a categorical feature. …
This post will be an introductory level on reinforcement learning. Throughout this post, the problem definitions and some most popular solutions will be discussed. After this article, you should be able to understand what is reinforcement learning, and how to find the optimal policy for the problem.