TensorFlow is an open source framework developed by Google researchers to run machine learning, deep learning and other statistical and predictive analytics workloads. Like similar platforms, it's designed to streamline the process of developing and executing advanced analytics applications for users such as data scientists, statisticians and predictive modelers.
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, Airflow, and MLflow.
Learn how to build Object Detection APIs through deploying a Flask application that runs TensorFlow. This video will show how to create two different REST APIs that will allow you to detect 80 different classes within images. This tutorial is great at demonstrating how to build APIs that could be used for a Web or Mobile application to run object detections.
No Python required - this session will highlight unique opportunities by bringing ML and linear algebra to Node.js with TensorFlow.js. Nick will highlight how you can get started using pre-trained models, train your own models, and run TensorFlow.js in various Node.js environments (server, IoT).
In this lesson, I will show how to use LSTM to generate text sequences on given seed text. Please like and subscribe to this channel to show your support.
Top 10 Machine Learning Frameworks for Web Development. There are many machine learning framework used for the web development company. The web development with machine learning is going to change the IT world in the future as it is becoming popular day by day. These frameworks are written in different languages such as Python, Java, C++, Scala, etc.
TensorFlow.js Image Classification Made Easy In this video you're going to discover an easy way how to train a convolutional neural network for image classification and use the created TensorFlow.js image classifier afterwards to score x-ray images locally in your web browser.
In this TensorFlow tutorial, you'll understand Eager execution runtime in TensorFlow. TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs. Eager execution supports most TensorFlow operations and GPU acceleration. Eager execution is a flexible machine learning platform for research and experimentation
Build a Deep Audio De-Noiser Using TensorFlow 2.0 .Speech denoising is a long-standing problem. Given a noisy input signal, the aim is to filter out such noise without degrading the signal of interest.
In this TensorFlow full course tutorial for Beginners will help you learn about Deep Learning with TensorFlow in detail, understand the basics of Deep Learning, how to install TensorFlow 2.0 on Ubuntu, how to use TensorFlow in Python, how to use TensorFlow object detection API to detect objects in images as well as videos
On this episode of TensorFlow Tutorial, Software Engineer Eugene Zhulenev demonstrates graph rewriting for functions in TensorFlow 2.0. Take an inside look into the TensorFlow team’s own internal training sessions--technical deep dives into TensorFlow by the very people who are building it!
Predicting the Stock price Using TensorFlow. A simple deep learning model for stock price prediction using TensorFlow
On this episode of Inside TensorFlow, Software Engineer Jacques Pienaar discusses MLIR: multi-level intermediate representation compiler infrastructure for TensorFlow developers.
How to Program UMAP from Scratch. And how to improve UMAP. Continue reading on Towards Data Science
Easy Image Classification with TensorFlow 2.0 ... Eager execution is enabled by default, without sacrificing the performance optimizations of graph-based execution. APIs are ... Tighter Keras integration as the high-level API.
NLP in TensorFlow 2.0/PyTorch.This blog post is dedicated to the use of the Transformers library using TensorFlow: using the Keras API as well as the TensorFlow TPUStrategy to fine-tune a State-of-The-Art Transformer model.