Learning Deep Learning: Unit 2

Learning Deep Learning: Unit 2

本课程是 Learning Deep Learning 专项课程的一部分

位教师：Pearson

访问权限由 New York State Department of Labor 提供

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Build and optimize convolutional neural networks for advanced image classification tasks using TensorFlow and PyTorch.
Apply recurrent neural networks and LSTMs to sequential data problems, including time series forecasting and text autocompletion.
Develop neural language models and implement word embeddings for robust natural language processing.
Design and implement encoder-decoder architectures and Transformer models for machine translation and sequence-to-sequence tasks.

您将获得的技能

要了解的详细信息

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作业

4 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Learning Deep Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有1个模块

This course covers advanced deep learning topics, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and modern language models. You will learn techniques for image classification, time series prediction, and natural language processing. The course includes building and optimizing CNNs for image recognition, using architectures such as AlexNet, VGGNet, GoogLeNet, and ResNet, and working with pre-trained models. You will also work with RNNs and LSTMs for tasks like forecasting and text autocompletion. The curriculum covers neural language models, word embeddings (such as Word2vec and wordpieces), encoder-decoder architectures, attention mechanisms, and Transformers for machine translation. Hands-on projects using TensorFlow and PyTorch will help you develop practical skills for solving real-world problems in computer vision and language processing.

This module provides a comprehensive introduction to advanced deep learning techniques for processing images and natural language. It covers convolutional neural networks for image classification, including architectures like AlexNet, VGGNet, GoogLeNet, and ResNet. The module then explores recurrent neural networks and LSTMs for time series and sequential data, followed by neural language models and word embeddings. Finally, it introduces encoder-decoder architectures, attention mechanisms, and Transformer models for neural machine translation, with practical implementations in TensorFlow and PyTorch throughout.

涵盖的内容

44个视频4个作业

44个视频总计341分钟

Topics 1分钟
The CIFAR-10 Dataset 4分钟
Convolutional Layer 9分钟
Building a Convolutional Neural Network 14分钟
Programming Example: Image Classification Using CNN with TensorFlow 9分钟
Programming Example: Image Classification Using CNN with PyTorch 9分钟
AlexNet 6分钟
VGGNet 5分钟
GoogLeNet 5分钟
ResNet 6分钟
Programming Example: Using a Pretrained Network with TensorFlow 4分钟
Programming Example: Using a Pretrained Network with PyTorch 5分钟
Amplifying Your Data 4分钟
Efficient CNNs 4分钟
Lesson 4 Summary 3分钟
Topics 1分钟
Problem Types Involving Sequential Data 7分钟
Recurrent Neural Networks 8分钟
Programming Example: Forecasting Book Sales with TensorFlow 9分钟
Programming Example: Forecasting Book Sales with PyTorch 11分钟
Backpropagation Through Time and Keeping Gradients Healthy 9分钟
Long Short-Term Memory 10分钟
Autoregression and Beam Search 7分钟
Programming Example: Text Autocompletion with TensorFlow 14分钟
Programming Example: Text Autocompletion with PyTorch 16分钟
Lesson 5 Summary 2分钟
Topics 1分钟
Language Models 13分钟
Word Embeddings 12分钟
Programming Example: Language Model and Word Embeddings with TensorFlow 12分钟
Programming Example: Language Model and Word Embeddings with PyTorch 18分钟
Word2vec 6分钟
Programming Example: Using Pretrained GloVe Embeddings 7分钟
Handling Out-of-Vocabulary Words with Wordpieces 3分钟
Lesson 6 Summary 2分钟
Topics 1分钟
Encoder–Decoder Network for Neural Machine Translation 4分钟
Programming Example: Neural Machine Translation with TensorFlow 24分钟
Programming Example: Neural Machine Translation with PyTorch 22分钟
Attention 8分钟
The Transformer 8分钟
Programming Example: Machine Translation Using Transformer with TensorFlow 7分钟
Programming Example: Machine Translation Using Transformer with PyTorch 8分钟
Lesson 7 Summary 2分钟

4个作业总计120分钟

Convolutional Neural Networks (CNN) and Image Classification Quiz 30分钟
Recurrent Neural Networks (RNN) and Time Series Prediction Quiz 30分钟
Neural Language Models and Word Embeddings Quiz 30分钟
Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation Quiz 30分钟