Master Deep Learning, and Break into AL


Master Deep Learning, and Break into A


About This Specialization

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

We will help you master Deep Learning, understand how to apply it, and build a career in AI.

Created by:

Industry Partners:


courses 5 courses

Follow the suggested order or choose your own.

projects Projects

Designed to help you practice and apply the skills you learn.

    certificates Certificates

Highlight your new skills on your resume or LinkedIn.

Projects Overview

You will see and work on case studies in healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will also build near state-of-the-art deep learning models for several of these applications. In a “Machine Learning flight simulator”, you will work through case studies and gain “industry-like experience” setting direction for an ML team. is also partnering with the NVIDIA Deep Learning Institute (DLI) to provide labs in advanced, application-specific topics and to give learners access to GPUs for programming assignments. This will give you an opportunity to build deep learning projects in a cutting-edge, industry-like environment.

  1. COURSE 1

    Neural Networks and Deep Learning

    Current session: Dec 18
    4 weeks of study, 3-6 hours a week
    English, Chinese (Traditional)

    About the 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.

    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.


    Video · Welcome


    Video · What is a neural network?


    Video · Supervised Learning with Neural Networks


    Video · Why is Deep Learning taking off?


    Video · About this Course


    Reading · Frequently Asked Questions


    Video · Course Resources


    Reading · How to use Discussion Forums


    Quiz · Introduction to deep learning


    Video · Geoffrey Hinton interview

    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.


    Video · Binary Classification


    Video · Logistic Regression


    Video · Logistic Regression Cost Function


    Video · Gradient Descent


    Video · Derivatives


    Video · More Derivative Examples


    Video · Computation graph


    Video · Derivatives with a Computation Graph


    Video · Logistic Regression Gradient Descent


    Video · Gradient Descent on m Examples


    Video · Vectorization


    Video · More Vectorization Examples


    Video · Vectorizing Logistic Regression


    Video · Vectorizing Logistic Regression’s Gradient Output


    Video · Broadcasting in Python


    Video · A note on python/numpy vectors


    Video · Quick tour of Jupyter/iPython Notebooks


    Video · Explanation of logistic regression cost function (optional)


    Quiz · Neural Network Basics


    Reading · Deep Learning Honor Code


    Reading · Programming Assignment FAQ


    Other · Python Basics with numpy (optional)


    Practice Programming Assignment · Python Basics with numpy (optional)


    Other · Logistic Regression with a Neural Network mindset


    Programming Assignment · Logistic Regression with a Neural Network mindset


    Video · Pieter Abbeel interview

    WEEK 3
    Shallow neural networks
    Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.


    Video · Neural Networks Overview


    Video · Neural Network Representation


    Video · Computing a Neural Network’s Output


    Video · Vectorizing across multiple examples


    Video · Explanation for Vectorized Implementation


    Video · Activation functions


    Video · Why do you need non-linear activation functions?


    Video · Derivatives of activation functions


    Video · Gradient descent for Neural Networks


    Video · Backpropagation intuition (optional)


    Video · Random Initialization


    Quiz · Shallow Neural Networks


    Other · Planar data classification with a hidden layer


    Programming Assignment · Planar data classification with a hidden layer


    Video · Ian Goodfellow interview

    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.


    Video · Deep L-layer neural network


    Video · Forward Propagation in a Deep Network


    Video · Getting your matrix dimensions right


    Video · Why deep representations?


    Video · Building blocks of deep neural networks


    Video · Forward and Backward Propagation


    Video · Parameters vs Hyperparameters


    Video · What does this have to do with the brain?


    Quiz · Key concepts on Deep Neural Networks


    Other · Building your Deep Neural Network: Step by Step


    Programming Assignment · Building your deep neural network: Step by Step


    Other · Deep Neural Network – Application


    Programming Assignment · Deep Neural Network Application

  2. COURSE 2

    Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

    Current session: Dec 18
    3 weeks, 3-6 hours per week
    English, Chinese (Traditional), Chinese (Simplified)

    About the Course

    This course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: – Understand industry best-practices for building deep learning applications. – Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking, – Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. – Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance – Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.

    WEEK 1
    Practical aspects of Deep Learning


    Video · Train / Dev / Test sets


    Video · Bias / Variance


    Video · Basic Recipe for Machine Learning


    Video · Regularization


    Video · Why regularization reduces overfitting?


    Video · Dropout Regularization


    Video · Understanding Dropout


    Video · Other regularization methods


    Video · Normalizing inputs


    Video · Vanishing / Exploding gradients


    Video · Weight Initialization for Deep Networks


    Video · Numerical approximation of gradients


    Video · Gradient checking


    Video · Gradient Checking Implementation Notes


    Quiz · Practical aspects of deep learning


    Other · Initialization


    Programming Assignment · Initialization


    Other · Regularization


    Programming Assignment · Regularization


    Other · Gradient Checking


    Programming Assignment · Gradient Checking


    Video · Yoshua Bengio interview

    WEEK 2
    Optimization algorithms


    Video · Mini-batch gradient descent


    Video · Understanding mini-batch gradient descent


    Video · Exponentially weighted averages


    Video · Understanding exponentially weighted averages


    Video · Bias correction in exponentially weighted averages


    Video · Gradient descent with momentum


    Video · RMSprop


    Video · Adam optimization algorithm


    Video · Learning rate decay


    Video · The problem of local optima


    Quiz · Optimization algorithms


    Other · Optimization


    Programming Assignment · Optimization


    Video · Yuanqing Lin interview

    WEEK 3
    Hyperparameter tuning, Batch Normalization and Programming Frameworks


    Video · Tuning process


    Video · Using an appropriate scale to pick hyperparameters


    Video · Hyperparameters tuning in practice: Pandas vs. Caviar


    Video · Normalizing activations in a network


    Video · Fitting Batch Norm into a neural network


    Video · Why does Batch Norm work?


    Video · Batch Norm at test time


    Video · Softmax Regression


    Video · Training a softmax classifier


    Video · Deep learning frameworks


    Video · TensorFlow


    Quiz · Hyperparameter tuning, Batch Normalization, Programming Frameworks


    Other · Tensorflow


    Programming Assignment · Tensorflow

  3. COURSE 3

    Structuring Machine Learning Projects

    Current session: Dec 18
    2 weeks of study, 3-4 hours/week

    About the Course

    You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team’s work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. This provides “industry experience” that you might otherwise get only after years of ML work experience. After 2 weeks, you will: – Understand how to diagnose errors in a machine learning system, and – Be able to prioritize the most promising directions for reducing error – Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance – Know how to apply end-to-end learning, transfer learning, and multi-task learning I’ve seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.

    WEEK 1
    ML Strategy (1)


    Video · Why ML Strategy


    Video · Orthogonalization


    Video · Single number evaluation metric


    Video · Satisficing and Optimizing metric


    Video · Train/dev/test distributions


    Video · Size of the dev and test sets


    Video · When to change dev/test sets and metrics


    Video · Why human-level performance?


    Video · Avoidable bias


    Video · Understanding human-level performance


    Video · Surpassing human-level performance


    Video · Improving your model performance


    Reading · Machine Learning flight simulator


    Quiz · Bird recognition in the city of Peacetopia (case study)


    Video · Andrej Karpathy interview

    WEEK 2
    ML Strategy (2)


    Video · Carrying out error analysis


    Video · Cleaning up incorrectly labeled data


    Video · Build your first system quickly, then iterate


    Video · Training and testing on different distributions


    Video · Bias and Variance with mismatched data distributions


    Video · Addressing data mismatch


    Video · Transfer learning


    Video · Multi-task learning


    Video · What is end-to-end deep learning?


    Video · Whether to use end-to-end deep learning


    Quiz · Autonomous driving (case study)


    Video · Ruslan Salakhutdinov interview

  4. COURSE 4

    Convolutional Neural Networks

    Current session: Dec 18
    4 weeks of study, 4-5 hours/week

    About the Course

    This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: – Understand how to build a convolutional neural network, including recent variations such as residual networks. – Know how to apply convolutional networks to visual detection and recognition tasks. – Know to use neural style transfer to generate art. – Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

    WEEK 1
    Foundations of Convolutional Neural Networks
    Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.


    Video · Computer Vision


    Video · Edge Detection Example


    Video · More Edge Detection


    Video · Padding


    Video · Strided Convolutions


    Video · Convolutions Over Volume


    Video · One Layer of a Convolutional Network


    Video · Simple Convolutional Network Example


    Video · Pooling Layers


    Video · CNN Example


    Video · Why Convolutions?


    Quiz · The basics of ConvNets


    Other · Convolutional Model: step by step


    Programming Assignment · Convolutional Model: step by step


    Other · Convolutional Model: application


    Programming Assignment · Convolutional model: application

    WEEK 2
    Deep convolutional models: case studies
    Learn about the practical tricks and methods used in deep CNNs straight from the research papers.


    Video · Why look at case studies?


    Video · Classic Networks


    Video · ResNets


    Video · Why ResNets Work


    Video · Networks in Networks and 1×1 Convolutions


    Video · Inception Network Motivation


    Video · Inception Network


    Video · Using Open-Source Implementation


    Video · Transfer Learning


    Video · Data Augmentation


    Video · State of Computer Vision


    Quiz · Deep convolutional models


    Other · Keras Tutorial – The Happy House (not graded)


    Other · Residual Networks


    Programming Assignment · Residual Networks

    WEEK 3
    Object detection
    Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.


    Video · Object Localization


    Video · Landmark Detection


    Video · Object Detection


    Video · Convolutional Implementation of Sliding Windows


    Video · Bounding Box Predictions


    Video · Intersection Over Union


    Video · Non-max Suppression


    Video · Anchor Boxes


    Video · YOLO Algorithm


    Video · (Optional) Region Proposals


    Quiz · Detection algorithms


    Other · Car detection with YOLOv2


    Programming Assignment · Car detection with YOLOv2

    WEEK 4
    Special applications: Face recognition & Neural style transfer
    Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!


    Video · What is face recognition?


    Video · One Shot Learning


    Video · Siamese Network


    Video · Triplet Loss


    Video · Face Verification and Binary Classification


    Video · What is neural style transfer?


    Video · What are deep ConvNets learning?


    Video · Cost Function


    Video · Content Cost Function


    Video · Style Cost Function


    Video · 1D and 3D Generalizations


    Quiz · Special applications: Face recognition & Neural style transfer


    Other · Art generation with Neural Style Transfer


    Programming Assignment · Art generation with Neural Style Transfer


    Other · Face Recognition for the Happy House


    Programming Assignment · Face Recognition for the Happy House

  5. COURSE 5

    Sequence Models

    Starts December 2017

    About the Course

    This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: – Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. – Be able to apply sequence models to natural language problems, including text synthesis. – Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.

Creators is dedicated to advancing AI by sharing knowledge about the field. We hope to welcome more individuals into deep learning and AI. is Andrew Ng’s new venture which amongst others, strives for providing comprehensive AI education beyond borders.


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