Télécharger [UDACITY] Deep Learning Nanodegree Program - [FCO] GloDLS torrent - GloDLS
Détails du Torrent Pour "[UDACITY] Deep Learning Nanodegree Program - [FCO] GloDLS"

[UDACITY] Deep Learning Nanodegree Program - [FCO] GloDLS

To download this torrent, you need a BitTorrent client: Vuze or BTGuard
Télécharger ce torrent
Download using Magnet Link

santé:
Seeds: 18
Leechers: 0
Terminé: 186 
Dernière vérification: 28-12-2021 12:09:11

Points de réputation Uploader : 16692





Write a Review for the Uploader:   235   Say Thanks with one good review:
Share on Facebook


Details
_NAME_:[UDACITY] Deep Learning Nanodegree Program - [FCO] GloDLS
Description:


Files Included: (380 WebRips (MP4) + Project Files (PDF, TXT, JPG, PY)
Language: English
Torrent Contains: 2,643 Files, 168 Folders
Course Source: https://eu.udacity.com/course/deep-learning-nanodegree--nd101

Description

Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to produce amazing solutions to important challenges.


Course Syllabus

 
Introduction

• Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.
 
Neural Networks

• Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.
 
Convolutional Neural Networks

• Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.
 
Recurrent Neural Networks

• Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.
 
Generative Adversarial Networks

• Learn to understand and implement the DCGAN model to simulate realistic images, with Ian Goodfellow, the inventor of GANS (generative adversarial networks).
 
Deploying a Sentiment Analysis Model

• Use deep neural networks to design agents that can learn to take actions in a simulated environment. Apply reinforcement learning to complex control tasks like video games and robotics.


Project 1

• Predicting Bike-Sharing Patterns

Project 2

• Dog-Breed Classifier

Project 3

• Generate TV scripts

Project 4

• Generate Faces

Project 5

• Deploying a Sentiment Analysis Model.

Why Take This Nanodegree Program?

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You'll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask. This is the ideal point-of-entry into the field of AI.




YouTube Video:
Catégorie:Tutorials
Langue :English  English
Taille totale:3.33 GB
Info Hash:F4AFBAC627A859C20F1E7E2B11B2D7789D3ED36C
Ajouté par:Prom3th3uS Super AdministratorMovie PirateVIP
Date:2018-11-21 20:29:21
Statut Torrent:Torrent Verified


évaluations:Not Yet Rated (Log in to rate it)


Tracker:
https://tracker.fastdownload.xyz:443/announce

Ce Torrent a également trackers de sauvegarde
URLSemoirsLeechersTerminé
https://tracker.fastdownload.xyz:443/announce000
udp://tw.opentracker.ga:36920/announce100
udp://tracker.tiny-vps.com:6969/announce000
https://seeders-paradise.org:443/announce000
udp://open.stealth.si:80/announce5018
udp://hk1.opentracker.ga:6969/announce100
udp://open.stealth.si:80/announce5018
https://opentracker.xyz:443/announce000
https://t.quic.ws:443/announce000
https://tracker.fastdownload.xyz:443/announce000
udp://tracker.opentrackr.org:1337/announce40150
udp://ipv4.tracker.harry.lu:80/announce200
udp://tracker.coppersurfer.tk:6969/announce000
udp://zephir.monocul.us:6969/announce000
udp://open.demonii.si:1337/announce000


Liste des fichiers: 





Comments
Aucun commentaire n'a encore publié