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[FreeCoursesOnline.Me] [Packt] PyTorch Deep Learning In 7 Days [FCO]
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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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FILE LIST
Filename
Size
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
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