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강의 계획 - 이 강좌에서 배울 내용

1

1

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Introduction to TensorFlow

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14개 동영상 (총 59분), 8 개의 읽기 자료
14개의 동영상
Welcome to week 11m
Hello TensorFlow!1m
[Coding tutorial] Hello TensorFlow!2m
What's new in TensorFlow 24m
Interview with Laurence Moroney5m
Introduction to Google Colab2m
[Coding tutorial] Introduction to Google Colab8m
TensorFlow documentation3m
TensorFlow installation3m
[Coding tutorial] pip installation3m
[Coding tutorial] Running TensorFlow with Docker10m
Upgrading from TensorFlow 13m
[Coding tutorial] Upgrading from TensorFlow 16m
8개의 읽기 자료
About Imperial College & the team10m
How to be successful in this course10m
Grading policy10m
Additional readings & helpful references10m
What is TensorFlow?10m
Google Colab resources10m
TensorFlow documentation10m
Upgrade TensorFlow 1.x Notebooks10m
2

2

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The Sequential model API

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13개 동영상 (총 59분)
13개의 동영상
What is Keras?1m
Building a Sequential model4m
[Coding tutorial] Building a Sequential model4m
Convolutional and pooling layers4m
[Coding tutorial] Convolutional and pooling layers5m
The compile method5m
[Coding tutorial] The compile method5m
The fit method4m
[Coding tutorial] The fit method7m
The evaluate and predict methods6m
[Coding tutorial] The evaluate and predict methods4m
Wrap up and introduction to the programming assignment1m
2개 연습문제
[Knowledge check] Feedforward and convolutional neural networks15m
[Knowledge check] Optimisers, loss functions and metrics15m
3

3

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Validation, regularisation and callbacks

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11개 동영상 (총 60분)
11개의 동영상
Interview with Andrew Ng6m
Validation sets4m
[Coding Tutorial] Validation sets9m
Model regularisation6m
[Coding Tutorial] Model regularisation4m
Introduction to callbacks5m
[Coding tutorial] Introduction to callbacks7m
Early stopping and patience6m
[Coding tutorial] Early stopping and patience5m
Wrap up and introduction to the programming assignment51
1개 연습문제
[Knowledge check] Validation and regularisation15m
4

4

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Saving and loading models

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12개 동영상 (총 74분)
12개의 동영상
Saving and loading model weights6m
[Coding tutorial] Saving and loading model weights10m
Model saving criteria4m
[Coding tutorial] Model saving criteria11m
Saving the entire model4m
[Coding tutorial] Saving the entire model8m
Loading pre-trained Keras models5m
[Coding tutorial] Loading pre-trained Keras models7m
TensorFlow Hub modules2m
[Coding tutorial] TensorFlow Hub modules8m
Wrap up and introduction to the programming assignment1m

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GETTING STARTED WITH TENSORFLOW 2의 최상위 리뷰

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TensorFlow 2 for Deep Learning 특화 과정 정보

This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models. The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. Prerequisite knowledge for this Specialization is python 3, general machine learning and deep learning concepts, and a solid foundation in probability and statistics (especially for course 3)....
TensorFlow 2 for Deep Learning

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