Expand your skill set and master TensorFlow. Customize your machine learning models through four hands-on courses!

4.8

별점

478개의 평가

Laurence Moroney +1명의 강사

제공자:

TensorFlow: Advanced Techniques 특화 과정deeplearning.ai

배울 내용

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.

귀하가 습득할 기술

Model InterpretabilityCustom Training LoopsCustom and Exotic ModelsGenerative Machine LearningObject DetectionFunctional APICustom LayersCustom and Exotic Models with Functional APICustom Loss FunctionsDistribution StrategiesBasic Tensor FunctionalityGradientTape for Optimization

이 전문 분야 정보

최근 조회 32,503회

About TensorFlow 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. About this Specialization 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. About you 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.

응용 학습 프로젝트

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.

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유동적 일정

유연한 마감을 설정하고 유지 관리합니다.

중급 단계

Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager

완료하는 데 약 5개월이 걸립니다

권장 학습 속도: 주 6시간

영어

자막: 영어

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 일정

유연한 마감을 설정하고 유지 관리합니다.

중급 단계

Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager

완료하는 데 약 5개월이 걸립니다

권장 학습 속도: 주 6시간

영어

자막: 영어

이 전문 분야에는 4개의 강좌가 있습니다.

강좌1

강좌 1

Custom Models, Layers, and Loss Functions with TensorFlow

4.8

별점

266개의 평가

•

71개의 리뷰

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.

강좌2

강좌 2

Custom and Distributed Training with TensorFlow

4.8

별점

86개의 평가

•

14개의 리뷰

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.

강좌3

강좌 3

Advanced Computer Vision with TensorFlow

4.8

별점

83개의 평가

•

20개의 리뷰

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.

강좌4

강좌 4

Generative Deep Learning with TensorFlow

4.9

별점

43개의 평가

•

4개의 리뷰

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.

강사

Laurence Moroney

Instructor

Lead AI Advocate, Google

287,152명의 학습자

14개의 강좌

Eddy Shyu

Senior Curriculum Developer

Product Lead, DeepLearning.AI

112,208명의 학습자

11개의 강좌

제공자:

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

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What are the applications of TensorFlow?
TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing.
Why is TensorFlow important?
TensorFlow is one of the most commonly used open-source libraries used for building and deploying ML models.
What is the TensorFlow specialization about?
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.
I’ve already completed the DeepLearning.AI TensorFlow Developer Professional Certificate. What else will I learn in this Specialization?
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.
What will I learn in this Specialization?
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.
Who is the TensorFlow Specialization for?
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.
What background knowledge is necessary?
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.
What will I be able to do upon completing the 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.
Who created the TensorFlow Specialization?
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.
Is this a standalone course or a Specialization?
This is a Specialization made up of 4 courses.
Do I need to take the courses in a specific order?
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
Can I audit this Specialization?
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.
How long does it take to complete the Specialization?
This Specialization consists of four courses. At the rate of 5 hours a week, it typically takes 3-4 weeks to complete each course.
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