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
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 🤖
Notes about TensorFlow taken from Hands-On Machine Learning with Scikit-Learn and TensorFlow
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.
📃 A curated list of awesome TensorFlow tutorial for beginner : https://tensorflow.studynote.life
TensorFlow Tutorial
Implementation of Deep Recurrent Q-Networks for Partially Observable environment setting in Tensorflow
Implementation of Model-based Reinforcement Learning Approach in Tensorflow
Intel-Tensorflow-course with my solutions
Tutorial materials to help you understand how to use TensorFlow.
친절한 한글 설명과 함께하는 텐서플로우 튜토리얼입니다!
CopyNet (Copy Mechanism in Seq2Seq) implementation with TensorFlow 2
Simple Tensorflow tutorials for learning by example
Created by Google Brain Team
Released November 9, 2015