Holczer Balazs
This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.
Section 1:
what are neural networks
modeling the human brain
the big picture
Section 2:
Hopfield neural networks
how to construct an autoassociative memory with neural networks
Section 3:
what is back–propagation
feedforward neural networks
optimizing the cost function
error calculation
backpropagation and gradient descent
Section 4:
the single perceptron model
solving linear classification problems
logical operators (AND and XOR operation)
Section 5:
applications of neural networks
clustering
classification (Iris–dataset)
optical character recognition (OCR)
smile–detector application from scratch
In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them.
If you are keen on learning methods, let’s get started!
Instructor Details
Courses : 24
Specification: Artificial Intelligence II – Hands-On Neural Networks (Java)
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8 reviews for Artificial Intelligence II – Hands-On Neural Networks (Java)
Add a review
| Price | $12.99 |
|---|---|
| Provider | |
| Duration | 5 hours |
| Year | 2020 |
| Level | All |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
$12.99
Trilok chitalia –
Clear understandable explanations
Jaime Mu oz Baena –
Los temas tratados clarifican muy bien los conceptos sobre Redes Neurales.
Gonzalo P rez Fern ndez –
Great experience, tons of knowledge in a very small amount of time and explained in a way that even a kid could tackle them. 5/5
Eitan Mizrahi –
The course is fine and enthusiasm, becuase the subject of AI is very attractive. The lecturer is fine (has quite knowledge), but get his knowledge by his own perspective, and not always the things are understood. Little disapponted by that I cannot download a document of some basic functions So I need to run all over again the whole course, and write in some notebook the formulas + some explanation. Some of the concept are intuative clear, and some of them not clear enough The lecturer provide way solving problem. Not always ways how he reached that calculations Better knowing what behind scense is important to understand. Also, give some quiz to check myself. I persume I failed if I tried passing a test just after the lecture. To summaraize the lecturer is fine, giving the most basic needed for learning AI. The rest need to work very hard to implements some basic AI algorithms, bulding the network! Maybe reading some more lectures and examples.
Anonymized User –
Good
Igor Delac –
Very clear, understandable. Teacher speaks a bit slow for my taste, so I had to adjust speed ratio to 1.25.
Stephen Prum –
Good match.
Rakesh Kumar –
Explained a complex topic with easily understandable terms.