Deep Learning vs. Machine Learning: A Beginner’s Guide
January 28, 2025
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Expand your skill set and master TensorFlow. Customize your machine learning models through four hands-on courses!
Instructors: Laurence Moroney
35,720 already enrolled
(1,504 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
(1,504 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.
Learn optimization and how to use GradientTape & Autograph, optimize training in different environments with multiple processors and chip types.
Practice object detection, image segmentation, and visual interpretation of convolutions.
Explore generative deep learning, and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to GANs.
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TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing.
Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.
This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate.
Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
Applied Learning Project
In this Specialization, you will gain practical knowledge of and hands-on training in advanced TensorFlow techniques such as style transfer, object detection, and generative machine learning.
Course 1: Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.
Course 2: Learn how optimization works and how to use GradientTape and Autograph. Optimize training in different environments with multiple processors and chip types.
Course 3: Practice object detection, image segmentation, and visual interpretation of convolutions.
Course 4: Explore generative deep learning and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to Generative Adversarial Networks.
In this course, you will:
• Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. • Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions. • Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
In this course, you will:
• Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools. • Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
In this course, you will:
a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images. c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more. d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
In this course, you will:
a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images. d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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TensorFlow is an end-to-end opensource platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications.
TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing.
TensorFlow is one of the most commonly used open-source libraries used for building and deploying ML models.
The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.
In this Specialization, you will expand your knowledge of the Functional API and build exotic non-sequential model types. You will learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. You will also explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.
The DeepLearning.AI TensorFlow Developer Professional Certificate equips you with the foundational knowledge to create entry-level TensorFlow models using the Sequential API and prepares you for the Google TensorFlow Developer Certificate exam.
In this new TensorFlow Specialization, you will expand your skill set and take your understanding of TensorFlow techniques to the next level. You will learn how to use the Functional API for custom training, custom layers, and custom models. You will be equipped to master TensorFlow in order to build powerful applications for complex scenarios.
Specialization: Gain practical knowledge of and hands-on training in advanced TensorFlow techniques such as style transfer (paint one picture in the style of another), object detection (detect where an object is in a picture), and generative machine learning (generating new images from scratch).
Course 1: Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers.
Course 2: Learn how optimization works and how to use GradientTape and Autograph. Optimize training in different environments with multiple processors and chip types.
Course 3: Practice object detection, image segmentation, and visual interpretation of convolutions.
Course 4: Explore generative deep learning and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to Generative Adversarial Networks.
The TensorFlow: Advanced Techniques Specialization provides an accessible pathway for early and mid-career software and machine learning engineers with a foundational knowledge of TensorFlow looking to master TensorFlow by teaching them how to use advanced features in order to expand their skill set and build powerful models.
Learners should have a working knowledge of AI and deep learning. They should have intermediate Python skills (understanding of decorators and context managers is preferred) as well as some experience with any deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.
We highly recommend that you complete the Deep Learning Specialization prior to starting this Specialization.
After completing this Specialization, you will gain practical knowledge and hands-on training in advanced TensorFlow techniques such as style transfer, object detection, and generative machine learning.
This Specialization was created by Laurence Moroney.
Laurence leads AI Advocacy at Google, with a vision to make AI easy for developers and to widen access to ML careers for everyone. He’s written dozens of programming books, the most recent being ‘AI and ML for Coders’ at O’Reilly. Laurence believes that MOOCs are one of the greatest ways to learn, and is excited to create TensorFlow Specializations with DeepLearning.AI on Coursera.
When not working with technology, he’s an active member of the Science Fiction Writers of America and has authored several sci-fi novels and comics books and a produced screenplay. Laurence is based in Washington state, where he drinks way too much coffee.
This is a Specialization made up of 4 courses.
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
You can audit the courses in the Specialization for free. You will not receive a certificate at the end if you choose to audit it for free instead of purchasing it.
This Specialization consists of four courses. At the rate of 5 hours a week, it typically takes 3-4 weeks to complete each course.
No
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Financial aid available,