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, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
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 | Beginner |
| Engagement | 3 weeks, 3-6 hours per week |
| Langue | English, Sous-titres : Chinese (Traditional), Chinese (Simplified) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Train / Dev / Test sets
- 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: Mini-batch gradient descent
- 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: Tuning process
- 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 |
| Obtenir un Certificat de Cours partageable | Disponible |
Great course, very complete and instructive! The programming exercices should yet be less guided
Very practical advice on how to fine tuning your neural networks
The Best
This course is a must study because fine-tuning models is way important in machine-learning.