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⇱ Learning Deep Learning: Unit 2 | Coursera

Learning Deep Learning: Unit 2

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Learning Deep Learning: Unit 2

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

Details to know

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Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Learning Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There is 1 module in this course

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.

What's included

44 videos4 assignments

44 videos•Total 341 minutes
  • Topics•1 minute
  • The CIFAR-10 Dataset•4 minutes
  • Convolutional Layer•9 minutes
  • Building a Convolutional Neural Network•14 minutes
  • Programming Example: Image Classification Using CNN with TensorFlow•9 minutes
  • Programming Example: Image Classification Using CNN with PyTorch•9 minutes
  • AlexNet•6 minutes
  • VGGNet•5 minutes
  • GoogLeNet•5 minutes
  • ResNet•6 minutes
  • Programming Example: Using a Pretrained Network with TensorFlow•4 minutes
  • Programming Example: Using a Pretrained Network with PyTorch•5 minutes
  • Amplifying Your Data•4 minutes
  • Efficient CNNs•4 minutes
  • Lesson 4 Summary•3 minutes
  • Topics•1 minute
  • Problem Types Involving Sequential Data•7 minutes
  • Recurrent Neural Networks•8 minutes
  • Programming Example: Forecasting Book Sales with TensorFlow•9 minutes
  • Programming Example: Forecasting Book Sales with PyTorch•11 minutes
  • Backpropagation Through Time and Keeping Gradients Healthy•9 minutes
  • Long Short-Term Memory•10 minutes
  • Autoregression and Beam Search•7 minutes
  • Programming Example: Text Autocompletion with TensorFlow•14 minutes
  • Programming Example: Text Autocompletion with PyTorch•16 minutes
  • Lesson 5 Summary•2 minutes
  • Topics•1 minute
  • Language Models•13 minutes
  • Word Embeddings•12 minutes
  • Programming Example: Language Model and Word Embeddings with TensorFlow•12 minutes
  • Programming Example: Language Model and Word Embeddings with PyTorch•18 minutes
  • Word2vec•6 minutes
  • Programming Example: Using Pretrained GloVe Embeddings•7 minutes
  • Handling Out-of-Vocabulary Words with Wordpieces•3 minutes
  • Lesson 6 Summary•2 minutes
  • Topics•1 minute
  • Encoder–Decoder Network for Neural Machine Translation•4 minutes
  • Programming Example: Neural Machine Translation with TensorFlow•24 minutes
  • Programming Example: Neural Machine Translation with PyTorch•22 minutes
  • Attention•8 minutes
  • The Transformer•8 minutes
  • Programming Example: Machine Translation Using Transformer with TensorFlow•7 minutes
  • Programming Example: Machine Translation Using Transformer with PyTorch•8 minutes
  • Lesson 7 Summary•2 minutes
4 assignments•Total 120 minutes
  • Convolutional Neural Networks (CNN) and Image Classification Quiz•30 minutes
  • Recurrent Neural Networks (RNN) and Time Series Prediction Quiz•30 minutes
  • Neural Language Models and Word Embeddings Quiz•30 minutes
  • Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation Quiz•30 minutes

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Instructors

Pearson
268 Courses•65,339 learners

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Frequently asked questions

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.

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