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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

包含在 Coursera Plus 中

了解更多

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

7 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

7 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

可分享的证书

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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。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

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340分钟

Topics1分钟
The CIFAR-10 Dataset4分钟
Convolutional Layer8分钟
Building a Convolutional Neural Network14分钟
Programming Example: Image Classification Using CNN with TensorFlow8分钟
Programming Example: Image Classification Using CNN with PyTorch9分钟
AlexNet5分钟
VGGNet5分钟
GoogLeNet5分钟
ResNet6分钟
Programming Example: Using a Pretrained Network with TensorFlow3分钟
Programming Example: Using a Pretrained Network with PyTorch4分钟
Amplifying Your Data3分钟
Efficient CNNs3分钟
Lesson 4 Summary2分钟
Topics1分钟
Problem Types Involving Sequential Data7分钟
Recurrent Neural Networks8分钟
Programming Example: Forecasting Book Sales with TensorFlow9分钟
Programming Example: Forecasting Book Sales with PyTorch10分钟
Backpropagation Through Time and Keeping Gradients Healthy8分钟
Long Short-Term Memory9分钟
Autoregression and Beam Search6分钟
Programming Example: Text Autocompletion with TensorFlow14分钟
Programming Example: Text Autocompletion with PyTorch15分钟
Lesson 5 Summary1分钟
Topics1分钟
Language Models13分钟
Word Embeddings12分钟
Programming Example: Language Model and Word Embeddings with TensorFlow12分钟
Programming Example: Language Model and Word Embeddings with PyTorch17分钟
Word2vec6分钟
Programming Example: Using Pretrained GloVe Embeddings6分钟
Handling Out-of-Vocabulary Words with Wordpieces3分钟
Lesson 6 Summary1分钟
Topics1分钟
Encoder–Decoder Network for Neural Machine Translation3分钟
Programming Example: Neural Machine Translation with TensorFlow24分钟
Programming Example: Neural Machine Translation with PyTorch21分钟
Attention8分钟
The Transformer8分钟
Programming Example: Machine Translation Using Transformer with TensorFlow7分钟
Programming Example: Machine Translation Using Transformer with PyTorch8分钟
Lesson 7 Summary1分钟

4个作业总计120分钟

Convolutional Neural Networks (CNN) and Image Classification Quiz30分钟
Recurrent Neural Networks (RNN) and Time Series Prediction Quiz30分钟
Neural Language Models and Word Embeddings Quiz30分钟
Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation Quiz30分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Pearson

268 门课程9,891 名学生

提供方

Pearson

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常见问题

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.