名前: | [UDACITY] PyTorch Scholarship Challenge - [FCO] GloDLS |
_DESCRIPTION_: Learn how to build deep neural networks with PyTorch and use it to implement state-of-the-art AI applications. Files Included: [166 WebRips (MP4) + Project Files (PDF, TXT, JPG, PY)] Torrent Contains: 869 Files, 37 Folders Course Source: https://in.udacity.com/facebook-pytorch-scholarship Content: Part 01 : Deep Learning with PyTorch Module 01: Lessons Lesson 01: Welcome to the Scholarship Challenge! Welcome to the course! Here we'll get you oriented and set you up for success in this course. Lesson 02: Introduction to Neural Networks Learn the concepts behind how neural networks operate and how we train them using data. Lesson 03: Talking PyTorch with Soumith Chintala Hear from Soumith Chintala, the creator of PyTorch, about the past, present, and future of the PyTorch framework. Lesson 04: Introduction to PyTorch Learn how to use PyTorch to build and train deep neural networks. By the end of this lesson, you will build a network that can classify images of dogs and cats with state-of-the-art performance. Lesson 05: Convolutional Neural Networks Learn how to use convolutional neural networks to build state-of-the-art computer vision models. Lesson 06: Style Transfer Use a deep neural network to transfer the artistic style of one image onto another image. Lesson 07: Recurrent Neural Networks Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time. Lesson 08: Sentiment Prediction with RNNs Here you'll build a recurrent neural network that can accurately predict the sentiment of movie reviews. Lesson 09: Deploying PyTorch Models In this lesson, we'll walk through a tutorial showing how to deploy PyTorch models with Torch Script. Lesson 10: Challenge Project Build and train a model that identifies flower species from images. Lab - Final Challenge. | |
YouTube動画: | |
カテゴリ: | Tutorials |
言語: | English |
合計サイズ: | 1.09 GB |
情報のハッシュ: | 1587DB75E7DB855B82A7586DF9445C932385B4D4 |
を追加することによって: | Prom3th3uS |
追加日: | 2018-11-25 01:25:08 |
トレントステータス: | Torrent Verified |
評価: | Not Yet Rated (Log in to rate it) |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/26. Notebook Gradient Descent.html | 9.64 kB |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/27. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt | 3.60 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/27. Perceptron vs Gradient Descent.html | 9.59 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/28. Continuous Perceptrons-07-JJ-aGEfM.en.vtt | 1.33 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/28. Continuous Perceptrons-07-JJ-aGEfM.mp4 | 1.13 MB |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/28. Continuous Perceptrons.html | 9.24 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/29. Non-Linear Data-F7ZiE8PQiSc.en.vtt | 633.00 B |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/38. Overfitting and Underfitting.html | 9.29 kB |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/39. Early Stopping.html | 9.23 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/39. Model Complexity Graph-NnS0FJyVcDQ.en.vtt | 5.32 kB |
Part 01-Module 01-Lesson 02_Introduction to Neural Networks/39. Model Complexity Graph-NnS0FJyVcDQ.mp4 | 4.90 MB |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/40. DL 53 Q Regularization-KxROxcRsHL8.en.vtt | 1.15 kB |
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Part 01-Module 01-Lesson 02_Introduction to Neural Networks/48. Learning Rate Decay.html | 9.19 kB |
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