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Neural Networks and Deep Learning
  • Level Professional
  • Duration 25 hours
  • Course by DeepLearning.AI
  • Offered by

About

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network's architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Modules

Welcome to the Deep Learning Specialization

    • 1 Videos
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1 Videos

  • Welcome

Introduction to Deep Learning

    • 4 Videos
    • 2 Readings
    • 1 ExternalTool
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1 External Tool

  • Intake Survey

4 Videos

  • What is a Neural Network?
  • Supervised Learning with Neural Networks
  • Why is Deep Learning taking off?
  • About this Course

2 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • Frequently Asked Questions

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W1

Quiz

    • 1 Assignment
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1 Assignment

  • Introduction to Deep Learning

Heroes of Deep Learning (Optional)

    • 1 Videos
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1 Videos

  • Geoffrey Hinton Interview

Logistic Regression as a Neural Network

    • 10 Videos
    • 1 Readings
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10 Videos

  • Binary Classification
  • Logistic Regression
  • Logistic Regression Cost Function
  • Gradient Descent
  • Derivatives
  • More Derivative Examples
  • Computation Graph
  • Derivatives with a Computation Graph
  • Logistic Regression Gradient Descent
  • Gradient Descent on m Examples

1 Readings

  • Derivation of DL/dz (Optional)

Python and Vectorization

    • 8 Videos
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8 Videos

  • Vectorization
  • More Vectorization Examples
  • Vectorizing Logistic Regression
  • Vectorizing Logistic Regression's Gradient Output
  • Broadcasting in Python
  • A Note on Python/Numpy Vectors
  • Quick tour of Jupyter/iPython Notebooks
  • Explanation of Logistic Regression Cost Function (Optional)

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W2

Quiz

    • 1 Assignment
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1 Assignment

  • Neural Network Basics

Programming Assignments

    • 3 Readings
    • 2 Programming
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2 Programming

  • Python Basics with Numpy
  • Logistic Regression with a Neural Network Mindset

3 Readings

  • Deep Learning Honor Code
  • Programming Assignment FAQ
  • (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace

Heroes of Deep Learning (Optional)

    • 1 Videos
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1 Videos

  • Pieter Abbeel Interview

Shallow Neural Network

    • 11 Videos
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11 Videos

  • Neural Networks Overview
  • Neural Network Representation
  • Computing a Neural Network's Output
  • Vectorizing Across Multiple Examples
  • Explanation for Vectorized Implementation
  • Activation Functions
  • Why do you need Non-Linear Activation Functions?
  • Derivatives of Activation Functions
  • Gradient Descent for Neural Networks
  • Backpropagation Intuition (Optional)
  • Random Initialization

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W3

Quiz

    • 1 Assignment
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1 Assignment

  • Shallow Neural Networks

Programming Assignment

    • 1 Programming
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1 Programming

  • Planar Data Classification with One Hidden Layer

Heroes of Deep Learning (Optional)

    • 1 Videos
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1 Videos

  • Ian Goodfellow Interview

Deep Neural Network

    • 8 Videos
    • 2 Readings
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8 Videos

  • Deep L-layer Neural Network
  • Forward Propagation in a Deep Network
  • Getting your Matrix Dimensions Right
  • Why Deep Representations?
  • Building Blocks of Deep Neural Networks
  • Forward and Backward Propagation
  • Parameters vs Hyperparameters
  • What does this have to do with the brain?

2 Readings

  • Optional Reading: Feedforward Neural Networks in Depth
  • Clarification For: What does this have to do with the brain?

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes W4

Quiz

    • 1 Assignment
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1 Assignment

  • Key Concepts on Deep Neural Networks

End of access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Programming Assignments

    • 1 Readings
    • 2 Programming
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2 Programming

  • Building your Deep Neural Network: Step by Step
  • Deep Neural Network - Application

1 Readings

  • Confusing Output from the AutoGrader

References & Acknowledgments

    • 2 Readings
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2 Readings

  • References
  • Acknowledgments

Auto Summary

Dive into the world of AI with "Neural Networks and Deep Learning," a comprehensive course designed by Coursera for professionals in Data Science & AI. Led by expert instructors, this program covers foundational concepts, including building, training, and applying deep neural networks. With a focus on practical applications, you'll learn to implement efficient neural networks and identify key architectural parameters. The course spans 1500 hours and offers various subscription options: Starter, Professional, and Paid. Ideal for those aiming to advance their technical careers and contribute to cutting-edge AI technology.

Andrew Ng
Instructors

Andrew Ng

Kian Katanforoosh
Instructors

Kian Katanforoosh

Younes Bensouda Mourri
Instructors

Younes Bensouda Mourri