Deep Learning Specialization

  • 5 courses
  • Intermediate
  • >
    4 months (5 hours/week)
  • >
    Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
  • >
    NVIDIA Deep Learning Institute

Skills you will gain

  • Tensorflow
  • Convolutional Neural Network
  • Artificial Neural Network
  • Deep Learning
  • Backpropagation
  • Python Programming
  • Hyperparameter
  • Hyperparameter Optimization
  • Machine Learning
  • Inductive Transfer
  • Multi-Task Learning
  • Facial Recognition System

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. 

In this Specialization, you will build 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. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. 

AI is transforming many industries. The Deep Learning Specialization 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. Along the way, you will 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 key parameters in architecture, and apply deep learning to your applications.
  2. Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
  3. Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning.
  4. Build a CNN, 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 or 3D data.
  5. Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering.


Course 1: Neural Networks and Deep Learning

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.


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.

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.

Week 3: Shallow neural networks

Learn to 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 it to computer vision.

Course 2: Improving Deep Neural Networks

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.


Week 1: Practical aspects of Deep Learning

Week 2: Optimization algorithms

Week 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course 3: Structuring Machine Learning Projects

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.


Week 1: ML Strategy (1)

Week 2: ML Strategy (2)

Course 4: Convolutional Neural Networks

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.


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.

Week 2: Deep convolutional models: case studies

Learn about the practical tricks and methods used in deep CNNs straight from the research papers.

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.

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!

Course 5: Sequence Models

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.


Week 1: Recurrent Neural Networks

Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.

Week 2: Natural Language Processing & Word Embeddings

Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.

Week 3: Sequence models & Attention mechanism

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

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 Founder

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain

Kian Katanforoosh Curriculum Developer

Lecturer of Computer Science at Stanford University,, Ecole CentraleSupelec

Younes Bensouda Mourri Teaching Assistant

Mathematical & Computational Sciences, Stanford University,

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    Frequently Asked Questions

    The course typically takes sixteen weeks of study, 3-6 hours a week, to complete.

    You can take the entire specialization for $49/month on Coursera. Alternatively, you can enroll in individual courses.