À propos de ce cours : If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.
Les destinataires de ce cours : Prerequisites: Expected: - Programming: Basic Python programming skills, with the capability to work effectively with data structures. Recommended: - Mathematics: Matrix vector operations and notation. - Machine Learning: Understanding how to frame a machine learning problem, including how data is represented will be beneficial. If you have taken my Machine Learning Course here, you have much more than the needed level of knowledge.
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 1 de 5 dans Deep Learning Specialization |
| Niveau | Intermédiaire |
| Engagement | 4 weeks of study, 3-6 hours a week |
| Langue | English, Sous-titres : Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, Japanese |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: What is a neural network?
- Vidéo: Supervised Learning with Neural Networks
- Vidéo: Why is Deep Learning taking off?
- Vidéo: About this Course
- Reading: Frequently Asked Questions
- Vidéo: Course Resources
- Reading: How to use Discussion Forums
- Vidéo: Geoffrey Hinton interview
- Vidéo: Logistic Regression
- Vidéo: Logistic Regression Cost Function
- Vidéo: Gradient Descent
- Vidéo: Derivatives
- Vidéo: More Derivative Examples
- Vidéo: Computation graph
- Vidéo: Derivatives with a Computation Graph
- Vidéo: Logistic Regression Gradient Descent
- Vidéo: Gradient Descent on m Examples
- Vidéo: Vectorization
- Vidéo: More Vectorization Examples
- Vidéo: Vectorizing Logistic Regression
- Vidéo: Vectorizing Logistic Regression's Gradient Output
- Vidéo: Broadcasting in Python
- Vidéo: A note on python/numpy vectors
- Vidéo: Quick tour of Jupyter/iPython Notebooks
- Vidéo: Explanation of logistic regression cost function (optional)
- Reading: Deep Learning Honor Code
- Reading: Programming Assignment FAQ
- Notebook: Python Basics with numpy (optional)
- Ungraded Programming: Python Basics with numpy (optional)
- Notebook: Logistic Regression with a Neural Network mindset
- Vidéo: Pieter Abbeel interview
- Vidéo: Neural Network Representation
- Vidéo: Computing a Neural Network's Output
- Vidéo: Vectorizing across multiple examples
- Vidéo: Explanation for Vectorized Implementation
- Vidéo: Activation functions
- Vidéo: Why do you need non-linear activation functions?
- Vidéo: Derivatives of activation functions
- Vidéo: Gradient descent for Neural Networks
- Vidéo: Backpropagation intuition (optional)
- Vidéo: Random Initialization
- Notebook: Planar data classification with a hidden layer
- Vidéo: Ian Goodfellow interview
- Vidéo: Forward Propagation in a Deep Network
- Vidéo: Getting your matrix dimensions right
- Vidéo: Why deep representations?
- Vidéo: Building blocks of deep neural networks
- Vidéo: Forward and Backward Propagation
- Vidéo: Parameters vs Hyperparameters
- Vidéo: What does this have to do with the brain?
- Notebook: Building your Deep Neural Network: Step by Step
- Notebook: Deep Neural Network - Application
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| 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 |
Awesome Jupyter Notebook Exercises
Great course, thanks for the opportunity
The instructor explains in a very detailed and clear way how to build a DL network from scratch. In addition, he explains important things and does a very quick and awesome introduction in calculating gradients using derivatives. IIf someone has programming experience in Python or other programming languages and wants to get deeper knowledge in ML algorithms, I can highly recommend it.
SK
An excellent introduction to Deep Learning. I was surprised by how well the course was structured and how much I was able to get out of it.