Course Summary
This course teaches you the fundamentals of neural networks and deep learning. You will learn how to build deep neural networks, implement vectorized neural networks, and apply deep learning to various applications.Key Learning Points
- Learn how to build and train deep neural networks
- Understand the key parameters in a neural network's architecture
- Apply deep learning to image recognition, speech recognition, and natural language processing
Related Topics for further study
Learning Outcomes
- Build and train deep neural networks
- Implement vectorized neural networks
- Apply deep learning to image recognition, speech recognition, and natural language processing
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Understanding of calculus and linear algebra
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Assignments
Similar Courses
- Applied AI with DeepLearning
- Deep Learning Specialization
- Convolutional Neural Networks
Related Education Paths
Notable People in This Field
- Geoffrey Hinton
- Yann LeCun
- Fei-Fei Li
Related Books
Description
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
Outline
- Introduction to Deep Learning
- Welcome
- What is a Neural Network?
- Supervised Learning with Neural Networks
- Why is Deep Learning taking off?
- About this Course
- Geoffrey Hinton Interview
- Frequently Asked Questions
- Lectures in PDF
- Introduction to Deep Learning
- Neural Networks Basics
- 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
- 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)
- Pieter Abbeel Interview
- Have questions? Meet us on Discourse!
- Derivation of DL/dz (Optional)
- Clarification of "dz"
- Lectures in PDF
- Deep Learning Honor Code
- Programming Assignment FAQ
- H​ow to Refresh your Workspace
- Neural Network Basics
- Shallow Neural Networks
- 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
- Ian Goodfellow Interview
- Lectures in PDF
- Shallow Neural Networks
- Deep Neural Networks
- 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?
- Clarification: What does this have to do with the brain?
- Lectures in PDF
- Confusing Output from the AutoGrader
- References
- Acknowledgments
- Key Concepts on Deep Neural Networks
Summary of User Reviews
Discover the power of neural networks and deep learning with this comprehensive course. Students rave about the quality of instruction, engaging lectures, and practical exercises. One key aspect that many users appreciate is the hands-on approach to learning, which allows students to apply what they've learned to real-world problems.Key Aspect Users Liked About This Course
Hands-on approach to learningPros from User Reviews
- High-quality instruction
- Engaging lectures
- Practical exercises
- Comprehensive coverage of neural networks and deep learning
- Real-world applications
Cons from User Reviews
- Some students feel the course is too fast-paced
- Requires a solid foundation in calculus and linear algebra
- Some users find the programming assignments challenging
- Occasional technical difficulties with the Coursera platform
- The course does not cover more advanced topics in deep learning
Andrew NgTop Instructor
4.9 Raiting
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