This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

You’ll learn Skills

This module gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

1. Understanding Neural Networks: You’ll learn the fundamentals of neural networks, including how they work, their components (neurons, layers, activation functions), and the basics of training neural networks.
2. Encoder-Decoder Architecture: You’ll gain a deep understanding of the encoder-decoder architecture, which is commonly used in sequence-to-sequence tasks. This includes understanding how the encoder extracts meaningful representations from input data, and how the decoder generates output sequences.
3. Sequence-to-Sequence Models: You’ll learn how to build and work with sequence-to-sequence models, which are essential for tasks like machine translation, text summarization, and speech recognition.
4. Attention Mechanisms: Many modern encoder-decoder architectures incorporate attention mechanisms. You’ll learn about different types of attention mechanisms, such as self-attention and multi-head attention, and how they improve the model’s ability to handle long sequences.
5. Recurrent Neural Networks (RNNs) and LSTMs: These are foundational components of many encoder-decoder models. You’ll learn about RNNs, LSTMs, and their variants, as well as how to implement them in practical applications.
6. Transformer Architecture: The transformer architecture, which underlies models like BERT and GPT, is a critical part of many encoder-decoder models. You’ll learn about transformers, how they work, and how to apply them to various NLP tasks.
7. Natural Language Processing (NLP): If your course focuses on NLP, you’ll learn about tokenization, word embeddings (e.g., Word2Vec, GloVe), and how to use pre-trained language models effectively.
8. Computer Vision Applications: Some encoder-decoder architectures are used in computer vision tasks, such as image captioning and image generation. You may learn how to apply these models to vision-related problems.
9. Hands-On Implementation: Practical skills are a significant part of such courses. You’ll likely gain experience in implementing encoder-decoder models using popular deep learning frameworks like TensorFlow or PyTorch.
10. Hyperparameter Tuning: You’ll learn techniques for optimizing model performance through hyperparameter tuning, which is crucial for getting the best results from your encoder-decoder models.

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