10-31-2024, 08:55 AM
Master Data Structures + Algorithms For Developers
![[Image: 5d9c6c8e0b8179bf512279419b40f1c9.jpeg]](https://i124.fastpic.org/big/2024/1030/c9/5d9c6c8e0b8179bf512279419b40f1c9.jpeg)
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 204.96 MB | Duration: 0h 57m
Learn the Data Structures and Algorithms for the interviews
What you'll learn
Become more confident and prepared for your next coding interview
Learn, implement and use different Algorithms
Learn, implement, and use different Data Structures
Learn everything you need to ace difficult coding interviews
Requirements
Need to have basic of coding knowledge
Description
Data Structures and Algorithms (DSA) are fundamental concepts in computer science that enable efficient data handling and problem-solving. Data structures are specific ways of organizing, managing, and storing data to facilitate access and modifications. Algorithms, on the other hand, are well-defined steps or procedures to solve a particular problem. Together, DSA provides the foundation for building efficient software solutions, optimizing performance, and enhancing scalability in computer programs.Algorithms are equally vital, with the primary goal of solving computational problems effectively. Sorting algorithms, such as quicksort, mergesort, and heapsort, organize data systematically, enabling faster searches and optimized storage. Searching algorithms like binary search allow faster look-up times in sorted data by halving the search space with each step, contrasting with linear search's sequential approach. Algorithms are often evaluated by their time and space complexity using Big O notation, which provides a metric for algorithm efficiency. This helps in selecting the optimal algorithm based on resource constraints, ensuring that applications run efficiently even as data scales.Mastering DSA empowers developers to write efficient code, reduce computational bottlenecks, and build scalable applications. As modern computing deals with increasingly vast datasets and complex systems, DSA remains essential for creating programs that are not only functional but also performant.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Types of Data Structures
Lecture 3 Floating Point Operations
Lecture 4 Get Format Instructions
Lecture 5 Model Compression
Lecture 6 Model Training
Lecture 7 Wrapper Creation
Anyone preparing for programming interviews,Software Developers
Screenshots
![[Image: fe2548f2ffac52c90f5be4cec2817c05.jpeg]](https://i124.fastpic.org/big/2024/1030/05/fe2548f2ffac52c90f5be4cec2817c05.jpeg)
Say "Thank You"
rapidgator.net:
ddownload.com:
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 204.96 MB | Duration: 0h 57m
Learn the Data Structures and Algorithms for the interviews
What you'll learn
Become more confident and prepared for your next coding interview
Learn, implement and use different Algorithms
Learn, implement, and use different Data Structures
Learn everything you need to ace difficult coding interviews
Requirements
Need to have basic of coding knowledge
Description
Data Structures and Algorithms (DSA) are fundamental concepts in computer science that enable efficient data handling and problem-solving. Data structures are specific ways of organizing, managing, and storing data to facilitate access and modifications. Algorithms, on the other hand, are well-defined steps or procedures to solve a particular problem. Together, DSA provides the foundation for building efficient software solutions, optimizing performance, and enhancing scalability in computer programs.Algorithms are equally vital, with the primary goal of solving computational problems effectively. Sorting algorithms, such as quicksort, mergesort, and heapsort, organize data systematically, enabling faster searches and optimized storage. Searching algorithms like binary search allow faster look-up times in sorted data by halving the search space with each step, contrasting with linear search's sequential approach. Algorithms are often evaluated by their time and space complexity using Big O notation, which provides a metric for algorithm efficiency. This helps in selecting the optimal algorithm based on resource constraints, ensuring that applications run efficiently even as data scales.Mastering DSA empowers developers to write efficient code, reduce computational bottlenecks, and build scalable applications. As modern computing deals with increasingly vast datasets and complex systems, DSA remains essential for creating programs that are not only functional but also performant.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Types of Data Structures
Lecture 3 Floating Point Operations
Lecture 4 Get Format Instructions
Lecture 5 Model Compression
Lecture 6 Model Training
Lecture 7 Wrapper Creation
Anyone preparing for programming interviews,Software Developers
Screenshots
Say "Thank You"
rapidgator.net:
ddownload.com:
