Programs > Deep Learning Specialization

Deep Learning Specialization

  • 5 courses
  • Intermediate
  • >
    6 months (5 hours/week)
  • >
    Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
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Skills you will gain

  • Tensorflow
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Transformers
  • Python Programming
  • Deep Learning
  • Backpropagation
  • Optimization
  • Hyperparameter Tuning
  • Machine Learning
  • Transfer Learning
  • Multi-Task Learning
  • Object Detection and Segmentation
  • Facial Recognition System
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Attention Models
  • Natural Language Processing

The Deep Learning Specialization is a 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.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

By the end of this program, you will be ready to: 

  1. Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.
  2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
  3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning.
  4. Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
  5. Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering.
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Syllabus

Course 1: Neural Networks and Deep Learning

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.

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Week 1: Introduction to Deep Learning

Understand the significant technological trends driving deep learning development and where and how it’s applied.

Week 2: Neural Networks Basics

Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.

Week 3: Shallow Neural Networks

Build a neural network with one hidden layer using forward propagation and backpropagation.

Week 4: Deep Neural Networks

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

Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply various optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence; and implement a neural network in TensorFlow.

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Week 1: Practical Aspects of Deep Learning

Discover and experiment with various initialization methods, apply L2 regularization and dropout to avoid model overfitting, and use gradient checking to identify errors in a fraud detection model.

Week 2: Optimization Algorithms

Develop your deep learning toolbox by adding more advanced optimizations, random mini-batching, and learning rate decay scheduling to speed up your models.

Week 3: Hyperparameter tuning, Batch Normalization, and Programming Frameworks

Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily and train a neural network on a TensorFlow dataset.

Course 3: Structuring Machine Learning Projects

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the “industry experience” that you might otherwise get only after years of ML work experience.

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Week 1: ML Strategy (1)

Use a machine learning flight simulator to learn how machine learning achieves human-level performance.

Week 2: ML Strategy (2)

Become familiar with the concepts of end-to-end learning, transfer learning, and multi-task learning.

Course 4: Convolutional Neural Networks

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

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Week 1: Foundations of Convolutional Neural Networks

Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.

Week 2: Deep Convolutional Models: Case Studies

Discover practical techniques and methods used in research papers to apply transfer learning to your own deep CNN.

Week 3: Object Detection

Apply your knowledge of CNNs to computer vision: object detection and semantic segmentation using self-driving car datasets.

Week 4: Special Applications: Face Recognition and Neural Style Transfer

Discover how CNNs can be applied to multiple fields, including art generation and face recognition, and implement your own algorithm to generate art and recognize faces.

Course 5: Sequence Models

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.

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Week 1: Recurrent Neural Networks

Discover recurrent neural networks (RNNs) and several of their variants, including LSTMs, GRUs and Bidirectional RNNs, all models that perform exceptionally well on temporal data.

Week 2: Natural Language Processing and Word Embeddings

Use word vector representations and embedding layers to train recurrent neural networks with an outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition, and neural machine translation.

Week 3: Sequence Models and the Attention Mechanism

Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs, explore speech recognition and how to deal with audio data, and improve your sequence models with the attention mechanism.

Week 4: Transformers

Build the transformer architecture and tackle natural language processing (NLP) tasks such as attention models, named entity recognition (NER) and Question Answering (QA).

Jan Zawadzki Data Scientist at Carmeq

“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”

Kritika Jalan Data Scientist at Corecompete Pvt. Ltd.

“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”

Chris Morrow Sr. Product Manager at Amazon

“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”

Program Instructors

Andrew Ng Instructor

Founder, DeepLearning.AI; Co-founder, Coursera

Kian Katanforoosh Curriculum Developer

Founder, Workera

Younes Bensouda Mourri Instructor

Instructor of AI, Stanford University

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    Frequently Asked Questions

    Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome.

    Over the last few years, the availability of computing power and the amount of data being generated have led to an increase in deep learning capabilities. Today, deep learning engineers are highly sought after, and deep learning has become one of the most in-demand technical skills as it provides you with the toolbox to build robust AI systems that just weren’t possible a few years ago. Mastering deep learning opens up numerous career opportunities.

    The Deep Learning Specialization is a 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.

    In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

    AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

    By the end of the Deep Learning Specialization, you will be able to:

    1. Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.
    2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
    3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning.
    4. Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
    5. Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering.

    Expected: 

    • Learners should have intermediate Python experience (e.g., basic programming skills, understanding of for loops, if/else statements, data structures such as lists and dictionaries).

     

    Recommended: 

    • Learners should have a basic knowledge of linear algebra (matrix-vector operations and notation).
    • Learners should have an understanding of machine learning concepts (how to represent data, what an ML model does, etc.)

    The Deep Learning Specialization is for early-career software engineers or technical professionals looking to master fundamental concepts and gain practical machine learning and deep learning skills.

    The Deep Learning Specialization consists of five courses. At the rate of 5 hours a week, it typically takes 5 weeks to complete each course except course 3, which takes about 4 weeks.

    The Deep Learning Specialization has been created by Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri. 

     

    Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world’s largest MOOC platform. 

     

    Kian Katanforoosh is the co-founder and CEO of Workera and a lecturer in the Computer Science department at Stanford University. Workera allows data scientists, machine learning engineers, and software engineers to assess their skills against industry standards and receive a personalized learning path. Kian is also the recipient of Stanford’s Walter J. Gores award (Stanford’s highest teaching award) and the Centennial Award for Excellence in teaching.

     

    Younes Bensouda Mourri completed his Bachelor’s in Applied Mathematics and Computer Science and Master’s in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford – Applied Machine Learning, Deep Learning, and Teaching AI – and taught two of them for a few years.

    • All existing assignments and autograders have been refactored and updated to TensorFlow 2 across Courses 1, 2, 4, and 5.
    • Three new network architectures are presented with new lectures and programming assignments:
      1. Course 4 includes MobileNet (transfer learning) and U-Net (semantic segmentation).
      2. Course 5, once updated, will include Transformers (Network Architecture, Named Entity Recognition, Question Answering).
    • For a detailed list of changes, please check out the DLS Changelog.

    1. Your certificates will carry over for any courses you’ve already completed. 
    2. If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again. 
    3. If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).
    4. If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines. Please save your work by downloading your existing notebooks before switching to the new version.
    5. If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.

    1. Your certificates will carry over for any courses you’ve already completed.
    2. If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.  

    The Deep Learning Specialization is made up of 5 courses.

    You can enroll in the Deep Learning Specialization on Coursera. You will watch videos and complete assignments on Coursera as well.

    We recommend taking the courses in the prescribed order for a logical and thorough learning experience. Course 3 can also be taken as a standalone course.

    A Coursera subscription costs $49 / month.

    Yes, Coursera provides financial aid to learners who cannot afford the fee.

    You can audit the courses in the Deep Learning Specialization for free. 

    Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

    1. Go to your Coursera account. 
    2. Click on My Purchases and find the relevant course or Specialization.
    3. Click Email Receipt and wait up to 24 hours to receive the receipt.
    4. You can read more about it here.

    Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

    You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.

     

    If you complete all n courses in the S12n and are subscribed to the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.