DeepLearning.AI - Neural Networks and Deep Learning
4.6 /5- Offered byCoursera
Neural Networks and Deep Learning at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Neural Networks and Deep Learning at Coursera Highlights
- 38% got a tangible career benefit
- 11% got a pay increase or promotion
- Earn a certification upon successful completion
- Flexible deadlines
Neural Networks and Deep Learning at Coursera Course details
- Data Scientists
- Machine Learning Engineers
- Biostatisticians
- Researchers
- Data Engineers
- 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 Neural Networks and Deep Learning course is by far one of the best deep learning courses available online. It is an intermediate-level course that is rated 4.9 out of 5 on Coursera. The learners are expected to have basic knowledge of Python, data structures, linear algebra, and ML to take this course
- You will learn about the major trends driving the rise of deep learning, how to set up a machine learning problem with a neural network mindset, and analyze the key computations underlying deep learning
- The course instructors have done an excellent job at making the syllabus easy to understand and follow. By the end of this course, you will have a solid foundation of deep learning skills which you can use to build, train, and apply fully connected deep neural networks
- The Neural Networks and Deep Learning Course is offered by DeepLearning.AI, a renowned education technology company
- The instructors of this course are Andrew Ng who is the founder of DeepLearning.AI and co-founder of Coursera, Kian Katanforoosh who is a senior curriculum developer, and Younes Bensouda Mourri who teaches Artificial Intelligence at Stanford University
Neural Networks and Deep Learning at Coursera Curriculum
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
Welcome
What is a neural network?
Supervised Learning with Neural Networks
Why is Deep Learning taking off?
About this Course
Course Resources
Geoffrey Hinton interview
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.
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
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
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
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.
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?
Neural Networks and Deep Learning at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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