Acerca de este curso: This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
Para quién es esta clase: - Learners that took course one, two, and four of the specialization. Course three is also recommended. - Anyone that already has a solid understanding of learning with neural networks, including convolutional networks, and wants to learn how to develop recurrent neural networks.
Enseñado por: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Enseñado por: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Enseñado por: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Información básica | Curso 5 de 5 en Deep Learning Specialization |
| Nivel | Intermediate |
| Idioma | English |
| Requerimientos de hardware | There is no hardware requirement |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Vídeo: Notation
- Vídeo: Recurrent Neural Network Model
- Vídeo: Backpropagation through time
- Vídeo: Different types of RNNs
- Vídeo: Language model and sequence generation
- Vídeo: Sampling novel sequences
- Vídeo: Vanishing gradients with RNNs
- Vídeo: Gated Recurrent Unit (GRU)
- Vídeo: Long Short Term Memory (LSTM)
- Vídeo: Bidirectional RNN
- Vídeo: Deep RNNs
- Notebook: Building a recurrent neural network - step by step
- Notebook: Dinosaur Island - Character-Level Language Modeling
- Notebook: Jazz improvisation with LSTM
- Vídeo: Using word embeddings
- Vídeo: Properties of word embeddings
- Vídeo: Embedding matrix
- Vídeo: Learning word embeddings
- Vídeo: Word2Vec
- Vídeo: Negative Sampling
- Vídeo: GloVe word vectors
- Vídeo: Sentiment Classification
- Vídeo: Debiasing word embeddings
- Notebook: Operations on word vectors - Debiasing
- Notebook: Emojify
- Vídeo: Picking the most likely sentence
- Vídeo: Beam Search
- Vídeo: Refinements to Beam Search
- Vídeo: Error analysis in beam search
- Vídeo: Bleu Score (optional)
- Vídeo: Attention Model Intuition
- Vídeo: Attention Model
- Vídeo: Speech recognition
- Vídeo: Trigger Word Detection
- Vídeo: Conclusion and thank you
- Notebook: Neural Machine Translation with Attention
- Notebook: Trigger word detection
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| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
A tough one. The notebook was very slow and and unstable.I like the original ML course's way of guiding us more. The suggestions on what should be done on every line make me lazy and I often don't focus hard enough on the intuition and math behind the code.
Great course!!!
The Jupyter notebook ,ust be improved. it has constant connection problems. it fails connection in between.
just as good as you could expect from the awesome Ng