Learning Deep TensorFlow End-To-End Process
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Updated
Aug 26, 2020 - Jupyter Notebook
TensorFlow is an open source library that was created by Google. It is used to design, build, and train deep learning models.
Learning Deep TensorFlow End-To-End Process
Fastest model building tutorial.
Notes from my process of learning tensorflow from Daniel Bourke's 64-hour tensorflow course
My experiment using data from Kaggle
Since the origin iris sample doesn't work with the new tensorflow(like 1.0, 0.12), so here is the fix version of that.
House prices dataset exploration and prediction. Workflow includes useful examples of Tensorflow pipelines including k-Nearest Neighbors imputer, Decision Tree Regression and XGBoost Regression
A guide to installing TensorFlow 2 🤖
CopyNet (Copy Mechanism in Seq2Seq) implementation with TensorFlow 2
📃 A curated list of awesome TensorFlow tutorial for beginner : https://tensorflow.studynote.life
Implementation of Deep Recurrent Q-Networks for Partially Observable environment setting in Tensorflow
Notes about TensorFlow taken from Hands-On Machine Learning with Scikit-Learn and TensorFlow
Implementation of Model-based Reinforcement Learning Approach in Tensorflow
TensorFlow Tutorial
Intel-Tensorflow-course with my solutions
Tutorial materials to help you understand how to use TensorFlow.
친절한 한글 설명과 함께하는 텐서플로우 튜토리얼입니다!
Simple Tensorflow tutorials for learning by example
Created by Google Brain Team
Released November 9, 2015