Latest Resources in TensorFlow

TensorFlow

TF Quant Finance: TensorFlow based Quant Finance Library by Google

Published January 19, 2021 under Quant Finance

This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being expanded over the next few months.

The library is structured along three tiers:

Foundational methods. Core mathematical methods – optimisation, interpolation, root finders, linear algebra, random and quasi-random number generation, etc.
Mid-level methods. ODE & PDE solvers, Ito process framework, Diffusion Path Generators, Copula samplers etc.
Pricing methods and other quant finance specific utilities. Specific Pricing models (e.g Local Vol (LV), Stochastic Vol (SV), Stochastic Local Vol (SLV), Hull-White (HW)) and their calibration. Rate curve building, payoff descriptions and schedule generation.

We aim for the library components to be easily accessible at each level. Each layer will be accompanied by many examples which can be run independently of higher level components.

How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch theaisummer.com

Published October 1, 2020 under Data Science

Training is without a doubt the most important part of developing a machine learning application. It’s when you start realizing whether or not your model is worth it, how your hyperparameters should look like and what do you need to change in your architecture. In general, most machine learning engineers spend quite some time on training, experimenting with different models, tuning their architecture and discovering the best metrics and losses for their problem.

How To Run TensorFlow Lite on Raspberry Pi for Object Detection youtube.com

Published November 28, 2019 under Machine Learning

TensorFlow Lite is a framework for running lightweight machine learning models, and it’s perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images.

TensorFlow 2.0 + Keras Overview for Deep Learning Researchers google.com

Published October 14, 2019 under Machine Learning

This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.

TensorFlow 2.0 Tutorial 01: Basic Image Classification lambdalabs.com

Published October 5, 2019 under Machine Learning

TensorFlow 2 is now live! This tutorial walks you through the process of building a simple CIFAR-10 image classifier using deep learning. In this tutorial, we will:

Define a model
Set up a data pipeline
Train the model
Accelerate training speed with multiple GPUs
Add callbacks for monitoring progress/updating learning schedules

The code in this tutorial is available here.

The fastest way to be productive with machine learning loominus.ai

Published July 24, 2019 under Machine Learning

Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.

Image Recognition in Python with TensorFlow and Keras

Published June 3, 2019 under Machine Learning

One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how.