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[FreeCoursesOnline.Me] [Packt] PyTorch Deep Learning in 7 Days [FCO]

[FreeCoursesOnline.Me] [Packt] PyTorch Deep Learning in 7 Days [FCO]

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Description




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/



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Our Forum for discussion >>> https://discuss.ftuforum.com/








File list
  • [FreeCoursesOnline.Me] [Packt] PyTorch Deep Learning in 7 Days [FCO]
  • 01.Getting started with PyTorch/0101.The Course overview.mp4 23.5 MB
  • 01.Getting started with PyTorch/0102.Quick Intro to PyTorch.mp4 31.8 MB
  • 01.Getting started with PyTorch/0103.Installation and Jupyter Notebook Setup.mp4 13.9 MB
  • 01.Getting started with PyTorch/0104.Tensors and Basic Tensor Operations.mp4 129.1 MB
  • 01.Getting started with PyTorch/0105.Advanced Tensor Operations.mp4 26.7 MB
  • 01.Getting started with PyTorch/0106.Loading and Saving Data.mp4 15.1 MB
  • 01.Getting started with PyTorch/0107.Assignment.mp4 4.1 MB
  • 02.Building a Neural Network/0201.Introduction to Neural Networks.mp4 13.6 MB
  • 02.Building a Neural Network/0202.Creating a Neural Network with PyTorch Sequential.mp4 80.4 MB
  • 02.Building a Neural Network/0203.Activations, Loss Functions, and Gradients.mp4 73.7 MB
  • 02.Building a Neural Network/0204.Forward and Backward Passes.mp4 69.8 MB
  • 02.Building a Neural Network/0205.Building a Network with nn.Module.mp4 137.3 MB
  • 02.Building a Neural Network/0206.Assignment.mp4 3 MB
  • 03.Regression and Classification/0301.Loading Structured Data for Classification.mp4 97.3 MB
  • 03.Regression and Classification/0302.Preprocessing Data.mp4 80.2 MB
  • 03.Regression and Classification/0303.Classification, Accuracy, and the Confusion Matrix.mp4 18.4 MB
  • 03.Regression and Classification/0304.Loading Structured Data for Regression.mp4 107.1 MB
  • 03.Regression and Classification/0305.Neural Networks for Regression.mp4 74.9 MB
  • 03.Regression and Classification/0306.Assignment.mp4 2.3 MB
  • 04.Implementing Convolutional Neural Networks/0401.Convolutional Networks for Image Analysis.mp4 12.3 MB
  • 04.Implementing Convolutional Neural Networks/0402.Convolutional Concepts Filters, Strides, Padding, and Pooling.mp4 5.9 MB
  • 04.Implementing Convolutional Neural Networks/0403.Implementing a Convolutional Network.mp4 13.1 MB
  • 04.Implementing Convolutional Neural Networks/0404.Visualizing Convolutional Network Layers.mp4 14.2 MB
  • 04.Implementing Convolutional Neural Networks/0405.Implementing an End-To-End Deep Convolutional Network.mp4 13.1 MB
  • 04.Implementing Convolutional Neural Networks/0406.Assignment.mp4 513.7 KB
  • 05.Implementing Transfer Learning/0501.Transfer Learning and Prebuilt Models.mp4 5.4 MB
  • 05.Implementing Transfer Learning/0502.Deep Learning with VGG.mp4 11.4 MB
  • 05.Implementing Transfer Learning/0503.Transfer Learning with VGG.mp4 15.6 MB
  • 05.Implementing Transfer Learning/0504.Transfer Learning with ResNet.mp4 24.4 MB
  • 05.Implementing Transfer Learning/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
  • 06.LSTM and Embedding for Natural Language Models/0602.Text Modeling with Bag-of-Words.mp4 7.9 MB
  • 06.LSTM and Embedding for Natural Language Models/0603.Sentiment Analysis with Bag-of-Words.mp4 16.1 MB
  • 06.LSTM and Embedding for Natural Language Models/0604.Sentiment Analysis with Word Embeddings.mp4 25.7 MB
  • 06.LSTM and Embedding for Natural Language Models/0605.Assignment.mp4 631.7 KB
  • 07.Deep Convolutional Generative Adversarial Networks/0701.Introduction to GANs and DCGANs.mp4 18.3 MB
  • 07.Deep Convolutional Generative Adversarial Networks/0702.Implementing DCGAN Model with PyTorch.mp4 14.5 MB
  • 07.Deep Convolutional Generative Adversarial Networks/0703.Training and Evaluating DCGAN on an Image Dataset.mp4 33 MB
  • 07.Deep Convolutional Generative Adversarial Networks/0704.Improving Performance.mp4 37.9 MB
  • 07.Deep Convolutional Generative Adversarial Networks/0705.Assignment.mp4 18.2 MB
  • Discuss.FTUForum.com.html 31.9 KB
  • Exercise Files/exercise_files.zip 813 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FTUForum.com.html 100.4 KB
  • How you can help Team-FTU.txt 235 B

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