Deep Learning and Neural NetWorks in PyTorch for Beginners
Category: Tutorials
- views: 197
- date: 15 February 2025
- posted by: AD-TEAM

Deep Learning and Neural Networks in PyTorch for Beginners
.MP4, AVC, 200 kbps, 1920x1080 | English, AAC, 128 kbps, 2 Ch | 2 hrs 8 mins | 372 MB
Instructor: Daniel We
Getting started with neural networks in PyTorch - Facebook's great neural network framework
Artificial intelligence (AI) is the hottest topic currently out there, there's no doubt about that. This is the future. Neural networks in particular have seen a lot of attention and they will be used everywhere -self driving cars, predictions in finance and sales forecasts - everywhere and across all industries. To be successful in the working world of tomorrow we have to expose ourselves to this interesting topic - and from the author's personal experience - coding your own neural network is the best way to understand how they work. Besides TensorFlow there is a new very interesting deep learning framework which is developed by Facebook - PyTorch. A challenge? Indeed. But together we can do it. Excited? I hope so. See you in the first lecture. Let's get into it.
What You Will Learn
You will learn the PyTorch fundamentals
You will find out how to code a deep neural network in Python
You will come to know what learning in neural networks means
You will be able to code a convolutional neural network in Python
If you have taken my neural networks in TensorFlow course you can compare both frameworks (TensorFlow and PyTorch)
More Info

TurboBit
RapidGator
AlfaFile
FileFactory
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
![]() |
Users of GUESTS are not allowed to comment this publication. | ||









