If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
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このコースについて
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
貴社では、従業員に需要のあるスキルを訓練させることによって利益を得ることができるでしょうか?
Coursera for Businessを試す学習内容
Handle real-world image data
Plot loss and accuracy
Explore strategies to prevent overfitting, including augmentation and dropout
Learn transfer learning and how learned features can be extracted from models
習得するスキル
- Inductive Transfer
- Augmentation
- Dropouts
- Machine Learning
- Tensorflow
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
貴社では、従業員に需要のあるスキルを訓練させることによって利益を得ることができるでしょうか?
Coursera for Businessを試すシラバス - 本コースの学習内容
Exploring a Larger Dataset
Augmentation: A technique to avoid overfitting
Transfer Learning
Multiclass Classifications
レビュー
- 5 stars78.70%
- 4 stars15.82%
- 3 stars3.63%
- 2 stars1.03%
- 1 star0.79%
CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW からの人気レビュー
Laurence Moroney is the best. Before taking up the course, i didnt know anything about the AI or ML or Tensorflow. The concepts were explained in such a manner that anyone can learn Tensorflow.
A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!
great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.
This course awesome, but the notebook from coursera "i think" doesn't support any experiment we want, so we have to do it on google colab. But great, limitation is okay as long it's still graded
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