Главная
Study mode:
on
1
Introduction
2
Basic Idea
3
Example
4
New Sample
5
Gradient Descent
6
Forward Pass
7
Learning Rate
8
Important Considerations
9
Fully Connected Neural Network
10
When do Neural Network become Deep
11
Computer Vision
12
Representation
13
Encoding
14
Filters
15
Filter multiplication
16
Convolutional layer
17
Weight
18
Stride
19
Pooling
20
Network Structure
21
Network Architecture
22
Transfer Learning
23
KNIME Hub
24
Nam Community
25
Book Recommendation
26
Webinars
Description:
Discover the fundamentals of deep learning and its practical application using the KNIME Analytics Platform in this informative webinar. Learn about key concepts such as artificial neurons and back-propagation, and gain insights into Convolutional Neural Networks for Computer Vision tasks. Follow along as a simple neural network is built to solve a classification problem using the KNIME Deep Learning - Keras Integration, demonstrating how to define, train, and deploy deep learning models without writing code. Explore the power of neural networks, from basic structures to deep architectures, and understand important considerations like gradient descent, learning rates, and transfer learning. Perfect for those looking to enter the world of deep learning without the coding barrier, this session provides a comprehensive introduction to building and implementing neural networks using a user-friendly, visual approach.

A Friendly Introduction to Codeless Deep Learning with KNIME Analytics Platform

Data Science Dojo
Add to list
0:00 / 0:00