Notes from my process of learning tensorflow from Daniel Bourke's 64-hour tensorflow course
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Updated
Feb 11, 2024 - Jupyter Notebook
TensorFlow is an open source library that was created by Google. It is used to design, build, and train deep learning models.
Notes from my process of learning tensorflow from Daniel Bourke's 64-hour tensorflow course
Implementation of Deep Recurrent Q-Networks for Partially Observable environment setting in Tensorflow
Fastest model building tutorial.
House prices dataset exploration and prediction. Workflow includes useful examples of Tensorflow pipelines including k-Nearest Neighbors imputer, Decision Tree Regression and XGBoost Regression
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
CopyNet (Copy Mechanism in Seq2Seq) implementation with TensorFlow 2
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
Simple Tensorflow tutorials for learning by example
Implementation of Model-based Reinforcement Learning Approach in Tensorflow
Automated Driving in NFS using CNN.
iPython notebook and Android app that shows how to build LSTM model in TensorFlow and deploy it on Android
📃 A curated list of awesome TensorFlow tutorial for beginner : https://tensorflow.studynote.life
Learning Deep TensorFlow End-To-End Process
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
A guide to installing TensorFlow 2 🤖
TensorFlow Tutorial
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Intel-Tensorflow-course with my solutions
Notes about TensorFlow taken from Hands-On Machine Learning with Scikit-Learn and TensorFlow
Created by Google Brain Team
Released November 9, 2015