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TORRENT DETAILS

[FreeCoursesOnline.Me] [UDACITY] Deep Learning Nanodegree Program - [FCO]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	f4afbac627a859c20f1e7e2b11b2d7789d3ed36c
Size:	3.3 GB in 99 files
Release Date(s):	Torrent Release: 21-11-2018 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

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.

For More Udemy Free Courses >>> http://www.freetutorials.eu

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.freetutorials.eu/

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	Part 01_Introduction to Deep Learning/Module 01_Introduction to the Nanodegree/Lesson 01_Welcome to Deep Learning/data.json	44.4 KB
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	Part 01_Introduction to Deep Learning/Module 01_Introduction to the Nanodegree/Lesson 04_Jupyter Notebooks/data.json	52.7 KB
	Part 01_Introduction to Deep Learning/Module 01_Introduction to the Nanodegree/Lesson 05_Matrix Math and NumPy Refresher/data.json	39.5 KB
	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 01_Introduction to Neural Networks/data.json	124.3 KB
	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 02_Implementing Gradient Descent/data.json	101.1 KB
	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 03_Training Neural Networks/data.json	18.1 KB
	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 04_GPU Workspaces Demo/data.json	19.5 KB
	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 05_Project Predicting Bike Sharing Data/data.json	9 KB
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	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 06_Sentiment Analysis/data.json	37 KB
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	Part 02_Neural Networks/Module 01_Neural Networks/Lesson 08_TensorFlow/data.json	114.4 KB
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	Part 03_Convolutional Networks/Module 01_ConvNets/Lesson 04_Weight Initialization/data.json	8.2 KB
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	Part 03_Convolutional Networks/Module 01_ConvNets/Lesson 06_Transfer Learning in TensorFlow/data.json	11.9 KB
	Part 03_Convolutional Networks/Module 01_ConvNets/Lesson 07_CNN Project Dog Breed Classifier/data.json	9.9 KB
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	Part 03_Convolutional Networks/Module 01_ConvNets/Lesson 08_Deep Learning for Cancer Detection with Sebastian Thrun/data.json	78 KB
	Part 04_Recurrent Networks/Module 01_Recurrent Neural Networks/Lesson 01_Recurrent Neural Networks/data.json	199.2 KB
	Part 04_Recurrent Networks/Module 01_Recurrent Neural Networks/Lesson 02_Long Short-Term Memory Networks (LSTM)/data.json	21.3 KB
	Part 04_Recurrent Networks/Module 01_Recurrent Neural Networks/Lesson 03_Implementation of RNN and LSTM/data.json	13.9 KB
	Part 04_Recurrent Networks/Module 01_Recurrent Neural Networks/Lesson 04_Hyperparameters/data.json	26 KB
	Part 04_Recurrent Networks/Module 01_Recurrent Neural Networks/Lesson 05_Embeddings and Word2vec/data.json	11.7 KB
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	Part 05_Generative Adversarial Networks/Module 01_Generative Adversarial Networks/Lesson 01_Generative Adversarial Networks/data.json	20.6 KB
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	Part 05_Generative Adversarial Networks/Module 01_Generative Adversarial Networks/Lesson 04_Semi-Supervised Learning/data.json	13.5 KB
	Part 06_Deep Reinforcement Learning/Module 01_Reinforcement Learning/Lesson 01_Introduction to RL/data.json	13.1 KB
	Part 06_Deep Reinforcement Learning/Module 01_Reinforcement Learning/Lesson 02_The RL Framework The Problem/data.json	83.5 KB
	Part 06_Deep Reinforcement Learning/Module 01_Reinforcement Learning/Lesson 03_The RL Framework The Solution/data.json	54.9 KB

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