Neural Networks Demystified

Course Feature
  • Cost
    Free
  • Provider
    Youtube
  • Certificate
    Paid Certification
  • Language
    English
  • Start Date
    On-Demand
  • Learners
    No Information
  • Duration
    1.00
  • Instructor
    /
Next Course
2.5
1 Ratings
Learn the fundamentals of neural networks and how to apply them to solve real-world problems. Get an intuitive understanding of the mathematics behind neural networks and gain the skills to build your own.
Show All
Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

This course, Neural Networks Demystified, provides an introduction to the complex topic of neural networks. Through a series of small videos, learners will gain an understanding of topics such as gradient descent, forward propagation, back propagation, and overfitting. This course is designed to demystify the concept of neural networks and provide a foundation for further exploration.

[Applications]
The application of this course can be seen in various fields such as computer vision, natural language processing, robotics, and many more. It can be used to create models that can accurately predict outcomes and classify data. It can also be used to create models that can detect patterns in data and make decisions based on those patterns. Additionally, it can be used to create models that can identify objects in images and videos. Finally, it can be used to create models that can generate text and audio.

[Career Paths]
[Recommended Career Path]Data Scientist

Data Scientists are responsible for analyzing large amounts of data and using it to develop insights and solutions to business problems. They use a variety of techniques, including machine learning, deep learning, and neural networks, to uncover patterns and trends in data. Data Scientists are expected to have a strong understanding of mathematics, statistics, and computer science, as well as the ability to communicate their findings to stakeholders.

The development trend of Data Scientists is very positive. As businesses become increasingly data-driven, the demand for Data Scientists is expected to grow. Companies are investing more in data-driven decision making, and Data Scientists are becoming increasingly important in helping organizations make informed decisions. Additionally, the development of new technologies such as artificial intelligence and machine learning is creating more opportunities for Data Scientists to apply their skills.

[Education Paths]
The recommended educational path for learners of this course is to pursue a degree in Artificial Intelligence (AI). AI is a rapidly growing field that focuses on the development of computer systems that can think and act like humans. AI is used in a variety of applications, from robotics to natural language processing.

A degree in AI typically involves courses in mathematics, computer science, and engineering. Students will learn about algorithms, data structures, and programming languages. They will also learn about machine learning, deep learning, and natural language processing. Additionally, they will learn about the ethical implications of AI and its potential applications.

The development trend of AI is rapidly evolving. AI is being used in more and more applications, from healthcare to finance. AI is also being used to automate processes and improve efficiency. As AI technology continues to advance, it is expected to become even more prevalent in our lives.

Show All
Recommended Courses
free ei-seminar-michael-carbin-the-lottery-ticket-hypothesis-12096
EI Seminar - Michael Carbin - The Lottery Ticket Hypothesis
2.0
Youtube 1 learners
Learn More
Learn from Michael Carbin, a renowned expert in Neural Networks, about the Lottery Ticket Hypothesis and its implications for training pruned networks. Explore the scalability challenges of larger-scale settings, and gain insight into the instability of linear mode connectivity. Discover the takeaways from our current understanding and the implications for follow-up research.
free introduction-to-soft-computing-12097
Introduction To Soft Computing
2.5
Youtube 5 learners
Learn More
This course will provide an introduction to the fundamentals of soft computing and its applications.
free stanford-seminar-concepts-and-questions-as-programs-12098
Stanford Seminar: Concepts and Questions as Programs
2.0
Youtube 2 learners
Learn More
This Stanford Seminar will help you understand the concepts and questions behind programming. Learn how to think like a programmer and develop the skills to create your own programs. Get ready to explore the world of programming and take your skills to the next level.
free generative-adversarial-networks-gans-and-stable-diffusion-12099
Generative Adversarial Networks (GANs) and Stable Diffusion
3.0
Youtube 2 learners
Learn More
This course provides an in-depth exploration of Generative Adversarial Networks (GANs) and Stable Diffusion. Learn how to build GANs with Tensorflow Keras, understand the differences between Generator and Discriminator, and explore the performance results of Stable Diffusion with KaraCV. Get ready to take your deep learning skills to the next level!
Favorites (0)
Favorites
0 favorite option

You have no favorites

Name delet