Convolutional Neural Networks with Pytorch

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Convolutional Neural Networks with Pytorch [TutsNode.com] - Convolutional Neural Networks with Pytorch 02 Convolutional Neural Networks with Pytorch
  • 004 Create Convolutional Neural Network model.mp4 (623.0 MB)
  • 015 course_code.zip (656.0 KB)
  • 004 Create Convolutional Neural Network model.en.srt (40.8 KB)
  • 002 Load datase.en.srt (31.0 KB)
  • 008 Analize model on test dataset and metrics.en.srt (24.7 KB)
  • 006 Train model_ train dataset.en.srt (23.7 KB)
  • 007 Train model_ validation dataset.en.srt (22.4 KB)
  • 003 Visualize data.en.srt (12.6 KB)
  • 001 Practical example presentation.en.srt (9.0 KB)
  • 009 Practical advices about training.en.srt (8.6 KB)
  • 005 Define cost function and optimizer.en.srt (4.4 KB)
  • 002 Load datase.mp4 (474.1 MB)
  • 008 Analize model on test dataset and metrics.mp4 (386.0 MB)
  • 006 Train model_ train dataset.mp4 (351.4 MB)
  • 007 Train model_ validation dataset.mp4 (330.1 MB)
  • 003 Visualize data.mp4 (217.4 MB)
  • 009 Practical advices about training.mp4 (101.9 MB)
  • 001 Practical example presentation.mp4 (90.2 MB)
  • 005 Define cost function and optimizer.mp4 (65.8 MB)
01 What is a Convolutional Neural Network_
  • 007 Overview of Convolutional Layer.en.srt (27.4 KB)
  • 011 Mathematical view of training a Neural Network.en.srt (21.6 KB)
  • 012 Practical view of training a Neural Network.en.srt (18.4 KB)
  • 004 Mathematical view of General Neural Networks.en.srt (15.6 KB)
  • 002 What is a Neural Network_.en.srt (13.8 KB)
  • 013 Train with batches.en.srt (13.5 KB)
  • 005 Type of Neural Networks models.en.srt (7.7 KB)
  • 001 Introduction.en.srt (7.5 KB)
  • 009 Overview of Fully Connected Layer.en.srt (6.3 KB)
  • 008 Overview of Pooling Layer.en.srt (5.9 KB)
  • 010 Typical Convolutional Neural Network architecture.en.srt (6.2 KB)
  • 003 Use cases of Neural Networks.en.srt (4.6 KB)
  • 006 Overview Convolutional Neural Network.en.srt (3.1 KB)
  • 007 Overview of Convolutional Layer.mp4 (323.1 MB)
  • 012 Practical view of training a Neural Network.mp4 (208.3 MB)
  • 011 Mathematical view of training a Neural Network.mp4 (206.4 MB)
  • 004 Mathematical view of General Neural Networks.mp4 (149.9 MB)
  • 002 What is a Neural Network_.mp4 (146.6 MB)
  • 013 Train with batches.mp4 (126.9 MB)
  • 005 Type of Neural Networks models.mp4 (83.3 MB)
  • 001 Introduction.mp4 (79.8 MB)
  • 009 Overview of Fully Connected Layer.mp4 (73.5 MB)
  • 010 Typical Convolutional Neural Network architecture.mp4 (68.8 MB)
  • 008 Overview of Pooling Layer.mp4 (66.2 MB)
  • 003 Use cases of Neural Networks.mp4 (52.2 MB)
  • 006 Overview Convolutional Neural Network.mp4 (33.9 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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Description


Description

In this course, you are going to learn what is and how to implement a Convolutional Neural Network model. To do this, you will use the library Pytorch using Python programming language, a tool that allows you to easily build a neural network model.

In the first part of the course, you will learn the basic about the theory of Deep Learning and Convolutional Neural Networks and then we will go deeper in the mathematics behind these models so that you can understand how these models works, how they are trained and evaluated and with this knowledge, you can change stuff in order to improve your own models.

After the theoretical part, you will build a practical project in which you will build a convolutional neural network model to classify images into different classes (like classify a picture of a dog as a dog). To do this you will use Pytorch, a library that allows you to create and train a neural network model using Python. First, you will create the model using a specific convolutional architecture, and then you will train the model by applying all the concepts you learned in the theoretical part. After training, you will get metrics that will allow you to analyze and evaluate how good your model is. Based on the results, you will learn how to implement changes into your project so that you can learn how to explore different options in order to improve the model performance.

You don’t need previous knowledge on Deep Learning, Convolutional Neural Networks or Pytorch, because you will learn the basics you need in order to build a deep learning project using this technology.

So, get started building convolutional deep learning projects with this very practical course.
Who this course is for:

Anyone who wants to learn about how to implement a convolutional neural network using Python and Pytorch
Anyone who wants to learn about how a Deep Learning project is developed

Requirements

Basic Python knowledge is required
No previous knowledge in Convolutional Neural Networks is required
No previous knowledge in Deep Learning is required
No previous knowledge in Machine Learning is required
No previous knowledge in Pytorch is required

Last Updated 4/2021



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Convolutional Neural Networks with Pytorch

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4.2 GB
seeders:19
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Convolutional Neural Networks with Pytorch

Torrent hash: A557571730FB6E71D057D712FB4DB5C0DEEF9155