Machine learning and data structures and algorithms are the two most popular concepts in computer science. With machine learning, we give our machines the ability to learn patterns from historical data. On the other hand, data structures are the concept used to efficiently store data and write optimized computer programs.
In interviewing many deep learning and machine learning candidates, we found that many respondents felt that the DS algorithm was unnecessary in machine learning interviews. But the truth is quite the opposite! In this article, we discuss five important reasons to learn data structures and algorithms for data science, machine learning, and deep learning.
When is real-time forecasting needed?
If the real systems did not work in real time, the result would be catastrophic; Therefore, industries dealing with machine learning technology are very concerned about the real-time performance of machine learning algorithms. Suppose you want to solve an object detection problem using machine learning algorithms.
For real-time performance, let's say you want 15 frames to run every second, that's 15 FPS, but your algorithm only gives you 10 FPS. For this reason, the prediction may be considered delayed, which could lead to poor user experience. Therefore, algorithms written with the knowledge of algorithm analysis can increase performance from 10 FPS to 15 FPS, allowing their object detection algorithm to work in real time.
Industries working on real machines need machine learning algorithms deployable on IoT devices
Edge devices like Arduino and Raspberry Pi are Internet of Things (IoT) devices widely used to integrate our code into real systems or machines. Due to the promising nature of ML algorithms, industries are increasingly turning to this technology. However, most of the solutions are difficult to implement on any peripheral device. Different companies like Facebook, Google and Deeplite. etc are working to reduce the complexity of ML algorithms. Therefore, with knowledge of data structures and algorithms, you can write efficient code that can be easily deployed to IoT devices and is useful for machine learning production.
It may happen that the libraries are not available or do not solve your problem
While you are working on the actual issues, there may be situations where you find that none of the libraries can help you solve your problem. Suppose we need to find the product of two matrices. However, if the product of two elements in this matrix multiplication exceeds a certain threshold, we must stop the process and discard these matrix pairs.
One way is to use libraries that already exist, perform full matrix multiplication, and compare the new matrix entries to the threshold. However, if the input tables are large, larger calculations may be required.
Machine learning and data structures and algorithms are the two most popular concepts in computer science. With machine learning, we give our machines the ability to learn patterns from historical data. On the other hand, data structures are the concept used to efficiently store data and write optimized computer programs.
In interviewing many deep learning and machine learning candidates, we found that many respondents felt that the DS algorithm was unnecessary in machine learning interviews. But the truth is quite the opposite! In this article, we discuss five important reasons to learn data structures and algorithms for data science, machine learning, and deep learning.
When is real-time forecasting needed?
If the real systems did not work in real time, the result would be catastrophic; Therefore, industries dealing with machine learning technology are very concerned about the real-time performance of machine learning algorithms. Suppose you want to solve an object detection problem using machine learning algorithms.
For real-time performance, let's say you want 15 frames to run every second, that's 15 FPS, but your algorithm only gives you 10 FPS. For this reason, the prediction may be considered delayed, which could lead to poor user experience. Therefore, algorithms written with the knowledge of algorithm analysis can increase performance from 10 FPS to 15 FPS, allowing their object detection algorithm to work in real time.
Industries working on real machines need machine learning algorithms deployable on IoT devices
Edge devices like Arduino and Raspberry Pi are Internet of Things (IoT) devices widely used to integrate our code into real systems or machines. Due to the promising nature of ML algorithms, industries are increasingly turning to this technology. However, most of the solutions are difficult to implement on any peripheral device. Different companies like Facebook, Google and Deeplite. etc are working to reduce the complexity of ML algorithms. Therefore, with knowledge of data structures and algorithms, you can write efficient code that can be easily deployed to IoT devices and is useful for machine learning production.
It may happen that the libraries are not available or do not solve your problem
While you are working on the actual issues, there may be situations where you find that none of the libraries can help you solve your problem. Suppose we need to find the product of two matrices. However, if the product of two elements in this matrix multiplication exceeds a certain threshold, we must stop the process and discard these matrix pairs.
One way is to use libraries that already exist, perform full matrix multiplication, and compare the new matrix entries to the threshold. However, if the input tables are large, larger calculations may be required.
Another option would be to use the knowledge of DS Algo and implement a matrix multiplication solution in less time. This saves you considerable IT costs. Another example can also relate to IoT devices. Suppose you wanted to implement your code where you used a signal filter library, say Scipy. As a library, scipy also includes several other features and as such can take up a lot of space on your Edge device, which makes it difficult for you to afford so much space for a single library. In this case, you can also create an optimal algorithm that the main library does not need.
When is it important to know how ML algorithms work?
Many students treat ML algorithms like a black box: you provide the input data to the algorithms, and they produce the output. They have mastered the art of using different algorithms for different problems. But what if we need an unconventional approach to solving a new problem? Therefore, in such scenarios, it may not be useful to think of ML algorithms as a black box.
So one of the best ideas would also be to learn how ML algorithms work? This can give us complete control over issues and provide additional information for developing new solutions. To understand the operating principles of these algorithms, knowledge of DS and Algo is essential. For example, a famous ML algorithm, Decision Tree, is a version of the tree data structure.
Algorithmic Thinking and Problem Solving Skills
Respondents like to ask something from DS Concepts for every IT-related role. This is no exception in the field of machine learning. Knowing the algorithms shows that one can imagine any problem and propose the best optimal solution. Also, it shows your strength in problem solving ability. So it can give you an extra edge when you appear or plan to appear in a machine learning interview.
There may be various other reasons which you can find somewhere, but we have tried to give you some concrete examples where we need knowledge about data structures and algorithms.
Popular data structure and algorithms used in machine learning and deep learning: matrix, vectors, matrices, linked list, binary trees, graph, stack, queue, hash, array, dynamic programming, greedy algorithms, random algorithms, etc.
Have fun learning, have fun with algorithms!