Главная
Study mode:
on
1
Intro
2
Context
3
Activation Gate
4
Can we reverse engineer this?
5
Learning by example
6
Robustness
7
Learning a Circuit / Board
8
Composing more complex circuits
9
Model: Composition of Layers
10
What is CNTK?
11
What does CNTK do?
12
Core components
13
What the hell is a Tensor?
14
Tensors
15
Functions
16
Sequential Models
17
Demo: MNIST dataset
18
MNIST input
19
Conclusion
20
What about TensorFlow? PyTorch?
21
Thank you :
Description:
Explore deep learning using Microsoft's Cognitive Toolkit (CNTK) and F# in this 55-minute conference talk. Delve into the powerful world of neural networks and their applications in computer vision, speech recognition, and automated language translation. Learn the fundamental concepts behind deep learning, understand its inner workings, and discover how F# simplifies the process of training and implementing models within .NET. Through practical demonstrations, gain insights into learning models from data and integrating them into your .NET projects. Examine topics such as activation gates, reverse engineering, learning by example, robustness, circuit learning, model composition, tensors, and sequential models. Witness a hands-on demo using the MNIST dataset and compare CNTK with other frameworks like TensorFlow and PyTorch.

Deep Learning with CNTK and F#

NDC Conferences
Add to list