À propos de ce cours : 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.
Les destinataires de ce cours : 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.
Enseigné par : Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Enseigné par : Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Enseigné par : Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Informations de base | Cours 2 de 5 dans Deep Learning Specialization |
| Niveau | Débutant |
| Engagement | 3 weeks, 3-6 hours per week |
| Langue | English, Sous-titres : Korean, Chinese (Traditional), Chinese (Simplified) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Bias / Variance
- Vidéo: Basic Recipe for Machine Learning
- Vidéo: Regularization
- Vidéo: Why regularization reduces overfitting?
- Vidéo: Dropout Regularization
- Vidéo: Understanding Dropout
- Vidéo: Other regularization methods
- Vidéo: Normalizing inputs
- Vidéo: Vanishing / Exploding gradients
- Vidéo: Weight Initialization for Deep Networks
- Vidéo: Numerical approximation of gradients
- Vidéo: Gradient checking
- Vidéo: Gradient Checking Implementation Notes
- Notebook: Initialization
- Notebook: Regularization
- Notebook: Gradient Checking
- Vidéo: Yoshua Bengio interview
- Vidéo: Understanding mini-batch gradient descent
- Vidéo: Exponentially weighted averages
- Vidéo: Understanding exponentially weighted averages
- Vidéo: Bias correction in exponentially weighted averages
- Vidéo: Gradient descent with momentum
- Vidéo: RMSprop
- Vidéo: Adam optimization algorithm
- Vidéo: Learning rate decay
- Vidéo: The problem of local optima
- Notebook: Optimization
- Vidéo: Yuanqing Lin interview
- Vidéo: Using an appropriate scale to pick hyperparameters
- Vidéo: Hyperparameters tuning in practice: Pandas vs. Caviar
- Vidéo: Normalizing activations in a network
- Vidéo: Fitting Batch Norm into a neural network
- Vidéo: Why does Batch Norm work?
- Vidéo: Batch Norm at test time
- Vidéo: Softmax Regression
- Vidéo: Training a softmax classifier
- Vidéo: Deep learning frameworks
- Vidéo: TensorFlow
- Notebook: Tensorflow
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
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One of the best courses on the internet for tuning your deep neural network.
This course helps practitioner or beginner to know how to tune supporting parameters in order to achieve more efficient/accurate NN. In other words, this course helps me figuring how to optimize the NN design, and I think this is recommended for beginners who like to explore Deep Learning/NN
excellent
Great course. To the point and lucid explanations. Would have liked a bit more elaboration on Tensorflow even though we can search the documentation ourselves.