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Neural Networks (ANN) using Keras and TensorFlow in Python


Build predictive deep learning models using Keras & Tensorflow Python, Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts. Instructor: Start Tech Academy Enroll Now - Neural Networks (ANN) using Keras and TensorFlow in Python About this Course You are looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right? You have found the right Neural Networks course! Add To Cart - GET COUPON CODE After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models.

Introduction to TensorFlow Lite TensorFlow


TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It enables on-device machine learning inference with low latency and a small binary size. TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API. TensorFlow Lite uses many techniques for achieving low latency such as optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels that allow smaller and faster (fixed-point math) models. Most of our TensorFlow Lite documentation is on Github for the time being.

#013 TF TensorFlow Lite Master Data Science 29.02.2020


Highlights: In this post we are going to show how to build a computer vision model and prepare it for deploying on mobile and embedded devices. Last time, we showed how we can improve a model performance using transfer learning. But why would we only use our model to predict images of cats or dogs on our computer when we can use it on our smartphone or other embedded device. TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It allows us to run machine learning models on mobile devices with low latency, quickly without need for accessing the server.

TensorFlow at Google I/O 2018! – TensorFlow – Medium


Over 7000 people attended I/O this year! TensorFlow was well represented with 7 talks and the AI & Machine Learning sandbox for attendees to explore what's new! TensorFlow has been extended to simplify model training and deployment using the JavaScript language. Watch this recap to get a detailed description on how to use JavaScript to train and deploy your models. In this session we introduced TensorFlow Extended (TFX), TensorFlow Hub, and announced new innovations and features in TensorFlow Serving.

What is the TensorFlow machine intelligence platform?


TensorFlow is an open source software library for numerical computation using data-flow graphs. It was originally developed by the Google Brain Team within Google's Machine Intelligence research organization for machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It reached version 1.0 in February 2017, and has continued rapid development, with 21,000 commits thus far, many from outside contributors. This article introduces TensorFlow, its open source community and ecosystem, and highlights some interesting TensorFlow open sourced models. It runs on nearly everything: GPUs and CPUs--including mobile and embedded platforms--and even tensor processing units (TPUs), which are specialized hardware to do tensor math on.