Informações sobre o curso: This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
Para quem é direcionado este curso: This class is for: - Learners that took the first course of the specialization: "Neural Networks and Deep Learning" - Anyone that already understands fully-connected neural networks, and wants to learn the practical aspects of making them work well.
Ministrado por: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Ministrado por: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Ministrado por: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Informações básicas | Curso 2 de 5 no Deep Learning Specialization |
| Nível | Beginner |
| Compromisso | 3 weeks, 3-6 hours per week |
| Idioma | English, Legendas: Chinese (Traditional), Chinese (Simplified) |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Vídeo: Bias / Variance
- Vídeo: Basic Recipe for Machine Learning
- Vídeo: Regularization
- Vídeo: Why regularization reduces overfitting?
- Vídeo: Dropout Regularization
- Vídeo: Understanding Dropout
- Vídeo: Other regularization methods
- Vídeo: Normalizing inputs
- Vídeo: Vanishing / Exploding gradients
- Vídeo: Weight Initialization for Deep Networks
- Vídeo: Numerical approximation of gradients
- Vídeo: Gradient checking
- Vídeo: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- Vídeo: Yoshua Bengio interview
- Vídeo: Understanding mini-batch gradient descent
- Vídeo: Exponentially weighted averages
- Vídeo: Understanding exponentially weighted averages
- Vídeo: Bias correction in exponentially weighted averages
- Vídeo: Gradient descent with momentum
- Vídeo: RMSprop
- Vídeo: Adam optimization algorithm
- Vídeo: Learning rate decay
- Vídeo: The problem of local optima
- Notebook: Optimization
- Vídeo: Yuanqing Lin interview
- Vídeo: Using an appropriate scale to pick hyperparameters
- Vídeo: Hyperparameters tuning in practice: Pandas vs. Caviar
- Vídeo: Normalizing activations in a network
- Vídeo: Fitting Batch Norm into a neural network
- Vídeo: Why does Batch Norm work?
- Vídeo: Batch Norm at test time
- Vídeo: Softmax Regression
- Vídeo: Training a softmax classifier
- Vídeo: Deep learning frameworks
- Vídeo: TensorFlow
- Notebook: Tensorflow
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| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
By itself, not really a couse. It should be part of the first one.
Like this course
Andrew is great at teaching. Quality of education is absolutely for 5 stars, but I am giving 4 because of technical difficulties with Jupiter notebook. Often happened that I wrote some code and it could not save, it just displayed error, so I had to copy code to my notepad and rerun the Jupiter notebook, and than copy the code back.
Really helpful !