Neural Networks and Deep Learning

Methode

Neural Networks and Deep Learning

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Beschreibung

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

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  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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'…

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Frequently asked questions

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Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Deep Learning, Python, Data Science, Machine Learning und Künstliche Intelligenz.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Who is this class for: 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.

Created by:  deeplearning.ai
  • Taught by:  Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Basic Info Course 1 of 5 in the Deep Learning Specialization Level Intermediate Commitment 4 weeks of study, 3-6 hours a week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.9 stars Average User Rating 4.9See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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deeplearning.ai deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.

Syllabus


WEEK 1


Introduction to deep learning
Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.


7 videos, 2 readings expand


  1. Video: Welcome
  2. Video: What is a neural network?
  3. Video: Supervised Learning with Neural Networks
  4. Video: Why is Deep Learning taking off?
  5. Video: About this Course
  6. Reading: Frequently Asked Questions
  7. Video: Course Resources
  8. Reading: How to use Discussion Forums
  9. Video: Geoffrey Hinton interview

Graded: Introduction to deep learning

WEEK 2


Neural Networks Basics
Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.


19 videos, 2 readings expand


  1. Video: Binary Classification
  2. Video: Logistic Regression
  3. Video: Logistic Regression Cost Function
  4. Video: Gradient Descent
  5. Video: Derivatives
  6. Video: Derivative Examples
  7. Video: Computation graph
  8. Video: Derivatives with a Computation Graph
  9. Video: Logistic Regression Gradient Descent
  10. Video: Gradient Descent on m Examples
  11. Video: Vectorization
  12. Video: Vectorization Examples
  13. Video: Vectorizing Logistic Regression
  14. Video: Vectorizing Logistic Regression's Gradient Output
  15. Video: Broadcasting in Python
  16. Video: A note on python/numpy vectors
  17. Video: Quick tour of Jupyter/iPython Notebooks
  18. Video: Explanation of logistic regression cost function (optional)
  19. Reading: Deep Learning Honor Code
  20. Reading: Programming Assignment FAQ
  21. Notebook: Python Basics with numpy (optional)
  22. Ungraded Programming: Python Basics with numpy (optional)
  23. Notebook: Logistic Regression with a Neural Network mindset
  24. Video: Pieter Abbeel interview

Graded: Neural Network Basics
Graded: Logistic Regression with a Neural Network mindset

WEEK 3


Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.


12 videos expand


  1. Video: Neural Networks Overview
  2. Video: Neural Network Representation
  3. Video: Computing a Neural Network's Output
  4. Video: Vectorizing across multiple examples
  5. Video: Explanation for Vectorized Implementation
  6. Video: Activation functions
  7. Video: Why do you need non-linear activation functions?
  8. Video: Derivatives of activation functions
  9. Video: Gradient descent for Neural Networks
  10. Video: Backpropagation intuition (optional)
  11. Video: Random Initialization
  12. Notebook: Planar data classification with a hidden layer
  13. Video: Ian Goodfellow interview

Graded: Shallow Neural Networks
Graded: Planar data classification with a hidden layer

WEEK 4


Deep Neural Networks
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.


8 videos expand


  1. Video: Deep L-layer neural network
  2. Video: Forward Propagation in a Deep Network
  3. Video: Getting your matrix dimensions right
  4. Video: Why deep representations?
  5. Video: Building blocks of deep neural networks
  6. Video: Forward and Backward Propagation
  7. Video: Parameters vs Hyperparameters
  8. Video: What does this have to do with the brain?
  9. Notebook: Building your Deep Neural Network: Step by Step
  10. Notebook: Deep Neural Network - Application

Graded: Key concepts on Deep Neural Networks
Graded: Building your deep neural network: Step by Step
Graded: Deep Neural Network Application

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