Packt | PyTorch Deep Learning in 7 Days [FCO]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.3 GB
  • Uploaded BySunRiseZone
  • Downloads257
  • Last checkedApr. 13th '19
  • Date uploadedApr. 12th '19
  • Seeders 24
  • Leechers12
Your ISP and Government can track your torrent activity! Hide your IP with a VPN!

Infohash : E556B7746F872327A502DF9E3C5D16B34B43ABDA



By : Will Ballard
Released : Saturday, March 30, 2019 [New Release!]
Torrent Contains : 47 Files, 8 Folders
Course Source : https://www.packtpub.com/big-data-and-business-intelligence/pytorch-deep-learning-7-days-video

Seven short lessons and a daily exercise, carefully chosen to get you started with PyTorch Deep Learning faster than other courses

Video Details

ISBN 9781789135367
Course Length 2 hour 9 minutes

Table of Contents

• GETTING STARTED WITH PYTORCH
• BUILDING A NEURAL NETWORK
• REGRESSION AND CLASSIFICATION
• IMPLEMENTING CONVOLUTIONAL NEURAL NETWORKS
• IMPLEMENTING TRANSFER LEARNING
• LSTM AND EMBEDDING FOR NATURAL LANGUAGE MODELS
• DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS

Video Description

PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks. PyTorch has a unique interface that makes it as easy to learn as NumPy.

This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks.

By the end of the course, you will be able to build Deep Learning applications with PyTorch.

All the code and supporting files for this course are available on GitHub at: https://github.com/PacktPublishing/PyTorch-Deep-Learning-in-7-Days

Style and Approach

This hands-on course will get you up-and-running with PyTorch in a week. It is composed of seven lessons. Each video covers one single concept or a set of code modules explained via step-by-step code walkthrough. The complete lesson is systematically explained and is followed by an assignment to spend time on your own as an exercise.

What You Will Learn

• Get comfortable with most commonly used PyTorch concepts, modules and API including Tensor operations, data representations, and manipulation
• Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers
• Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing
• Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems
• Implement state of the art in Natural Language Processing to solve real-world problems such as sentiment analysis
• Implement a simple Generative Adversarial Network to generate fancy images after training on a large image dataset

Authors

Will Ballard

Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America). https://www.linkedin.com/in/will-ballard-b09115/

For More Udemy Free Courses >>> https://ftuforum.com/
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.ftuforum.com/



Files:

[FreeCoursesOnline.Me] [Packt] PyTorch Deep Learning in 7 Days [FCO] 01.Getting started with PyTorch
  • 0101.The Course overview.mp4 (23.5 MB)
  • 0102.Quick Intro to PyTorch.mp4 (31.8 MB)
  • 0103.Installation and Jupyter Notebook Setup.mp4 (13.9 MB)
  • 0104.Tensors and Basic Tensor Operations.mp4 (129.1 MB)
  • 0105.Advanced Tensor Operations.mp4 (26.7 MB)
  • 0106.Loading and Saving Data.mp4 (15.1 MB)
  • 0107.Assignment.mp4 (4.1 MB)
02.Building a Neural Network
  • 0201.Introduction to Neural Networks.mp4 (13.6 MB)
  • 0202.Creating a Neural Network with PyTorch Sequential.mp4 (80.4 MB)
  • 0203.Activations, Loss Functions, and Gradients.mp4 (73.7 MB)
  • 0204.Forward and Backward Passes.mp4 (69.8 MB)
  • 0205.Building a Network with nn.Module.mp4 (137.3 MB)
  • 0206.Assignment.mp4 (3.0 MB)
03.Regression and Classification
  • 0301.Loading Structured Data for Classification.mp4 (97.3 MB)
  • 0302.Preprocessing Data.mp4 (80.2 MB)
  • 0303.Classification, Accuracy, and the Confusion Matrix.mp4 (18.4 MB)
  • 0304.Loading Structured Data for Regression.mp4 (107.1 MB)
  • 0305.Neural Networks for Regression.mp4 (74.9 MB)
  • 0306.Assignment.mp4 (2.3 MB)
04.Implementing Convolutional Neural Networks
  • 0401.Convolutional Networks for Image Analysis.mp4 (12.3 MB)
  • 0402.Convolutional Concepts Filters, Strides, Padding, and Pooling.mp4 (5.9 MB)
  • 0403.Implementing a Convolutional Network.mp4 (13.1 MB)
  • 0404.Visualizing Convolutional Network Layers.mp4 (14.2 MB)
  • 0405.Implementing an End-To-End Deep Convolutional Network.mp4 (13.1 MB)
  • 0406.Assignment.mp4 (513.7 KB)
05.Implementing Transfer Learning
  • 0501.Transfer Learning and Prebuilt Models.mp4 (5.4 MB)
  • 0502.Deep Learning with VGG.mp4 (11.4 MB)
  • 0503.Transfer Learning with VGG.mp4 (15.6 MB)
  • 0504.Transfer Learning with ResNet.mp4 (24.4 MB)
  • 0505.Assignment.mp4 (902.6 KB)
06.LSTM and Embedding for Natural Language Models
  • 0601.Recurrent Networks, RNN, and LSTM, GRU.mp4 (8.3 MB)
  • 0602.Text Modeling with Bag-of-Words.mp4 (7.9 MB)
  • 0603.Sentiment Analysis with Bag-of-Words.mp4 (16.1 MB)
  • 0604.Sentiment Analysis with Word Embeddings.mp4 (25.7 MB)
  • 0605.Assignment.mp4 (631.7 KB)
07.Deep Convolutional Generative Adversarial Networks
  • 0701.Introduction to GANs and DCGANs.mp4 (18.3 MB)
  • 0702.Implementing DCGAN Model with PyTorch.mp4 (14.5 MB)
  • 0703.Training and Evaluating DCGAN on an Image Dataset.mp4 (33.0 MB)
  • 0704.Improving Performance.mp4 (37.9 MB)
  • 0705.Assignment.mp4 (18.2 MB)
Exercise Files
  • exercise_files.zip (813.0 KB)
  • Discuss.FTUForum.com.html (31.9 KB)
  • FreeCoursesOnline.Me.html (108.3 KB)
  • FTUForum.com.html (100.4 KB)
  • How you can help Team-FTU.txt (0.2 KB)
  • [TGx]Downloaded from torrentgalaxy.org.txt (0.5 KB)
  • Torrent Downloaded From GloDls.to.txt (0.1 KB)

Code:

  • https://tracker.fastdownload.xyz:443/announce
  • udp://tracker.torrent.eu.org:451/announce
  • udp://tracker.cyberia.is:6969/announce
  • udp://tracker.leechers-paradise.org:6969/announce
  • udp://open.stealth.si:80/announce
  • udp://exodus.desync.com:6969/announce
  • udp://explodie.org:6969/announce
  • https://opentracker.xyz:443/announce
  • https://t.quic.ws:443/announce
  • udp://9.rarbg.to:2710/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://tracker.coppersurfer.tk:6969/announce
  • udp://tracker.internetwarriors.net:1337/announce
  • udp://open.demonii.si:1337/announce