explanation
Optimizing Data Structures, Algorithms, and Machine Learning LiveLessons (video training) is a course in learning data structures, algorithms, and machine learning optimization.
In Data Structures, Algorithms, and Machine Learning Optimization LiveLessons (video training) you will learn:
- It accounts for the effects of time and space using the Big O notation, based on an algorithm that can choose the best way to solve a machine learning problem using existing hardware resources.
- Familiarize yourself with the full range of Python data structures, including lists, dictionaries, trees, and graph-based structures.
- Develop an understanding of all essential data algorithms, including searching, sorting, hashing, and navigation.
- Learn how statistical methods and machine learning work for various optimizations.
- Understand what a multidimensional descent gradient optimization algorithm is and how to use it.
Course specifications
Posted by InformIT
teacher: john crone
Language:English
Level: Average
Lesson: 66
Duration: 6 hours 28 minutes
Course Topics:
Lesson 1: Data Structures and Algorithms Orientation
subject
1.1 Machine Learning Basics Series Orientation
1.2 A brief history of the data
1.3 A brief history of the algorithm
1.4 Application to machine learning
Lesson 2: “Big O” notation
subject
2.1 Introduction
2.2 Constant time
2.3 Linear Time
2.4 Polynomial Time
2.5 Common Runtime
2.6 Best case and worst case
Lesson 3: List-Based Data Structures
subject
3.1 List
3.2 Array
3.3 Linked list
3.4 Doubly linked list
3.5 stack
3.6 Queue
3.7 Deck
Lesson 4: Searching and Sorting
subject
4.1 Binary search
4.2 Bubble sort
4.3 Merge sort
4.4 Quick Sort
Lesson 5: Sets and Hashing
subject
5.1 Maps and Dictionaries
5.2 sets
5.3 Hash function
5.4 Conflict
5.5 load factor
5.6 Hash Map
5.7 String Key
5.8 ML of hashing
Lesson 6: Trees
subject
6.1 Introduction
6.2 Decision tree
6.3 Random Forest
6.4
6.5 Additional concepts
Unit 7: Graphs
subject
7.1 Introduction
7.2 Directed and undirected graphs
7.3 DAG: Directed Acyclic Graph
7.4 Additional concepts
7.5 Bonus: Pandas DataFrames
7.6 Data for further DSA research
Lesson 8: Machine Learning Optimization
subject
8.1 Statistics and machine learning
8.2 Objective function
8.3 Mean absolute error
8.4 Mean square error
8.5 Cost minimization using gradient descent
8.6 Gradient descent from scratch using PyTorch
8.7 Important notes
8.8 Stochastic Gradient Descent
8.9Learning
Maximized by slope rise
Lesson 9: Advanced Deep Learning Optimizer
subject
9.1 Jacobian matrix
9.2 Quadratic Optimization and Hessian
9.3 Momentum
9.4 Adaptive Optimizer
9.5 Celebration and Next Steps
summary
Course Prerequisites:
math: Once you become familiar with middle school level math, the lessons will be easier to follow. If you’re good at dealing with quantitative information, such as understanding charts and rearranging simple equations, you should be well prepared to keep up with all the math.
programming: All code demos are in Python, so having experience with Python or another object-oriented programming language will help you follow along with the hands-on examples.
movie
sample film
installation manual
After extracting, watch with your favorite players.
Subtitles: None
Quality: 720p
download link
File password: free download software
size
9.3GB