28 verified PDFs
Tensorflow can leverage both CPU & GPU resources to reduce training time. Define use case criteria & Implement use cases • Provide guidance on additional value ...
This will include an in depth description of many of the most important pieces of the TensorFlow API. We’ll also show how you can take a visual graph representation of a model and translate it into TensorFlow code, as well as verify that the graph is modeled correctly by using TensorBoard.
This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
The study shows that the inference time for a suitably optimized TensorFlow enabled solution architecture is significantly lower than for an unquantized model ...
Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance- based versus model-based learning.
This book will provide an introduction to the fundamentals of machine learning through Tensorflow. Tensorflow is Google's new software library for deep-learning ...
We describe an AI-based multi-agent system that we built to support automatic migration of TensorFlow-based deep learning models into JAX-based ones.
Abstract. In this research, we address two contemporary management paradoxes in the high-tech sector: competition versus cooperation and open-source versus ...
This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, such as IBM zSystems Integrated Accelerator for AI, and demonstrates how to leverage certain capabilities and components to enable solutions in business-critical use cases, such as fraud detection and credit risk scoring on the platform.
TensorFlow abstracts the operations in an ML application thus allowing programmers to focus on the high-level pro- gramming logic.
Learn to write clean, reusable code, manipulate data with NumPy and Pandas, and visualize your results—skills that form the foundation of any deep learning workflow. Explore how to find, load, and customize pretrained models from TensorFlow Hub and Keras Applications.
Our study collected 175 TensorFlow coding bugs from GitHub issues and StackOverflow questions. We analyzed these bugs quanti- tatively and qualitatively, and ...
Together these use cases have the potential to remove tens of thousands of contacts per week across all inbound channels, reducing queue times and allowing ...
Abstract—This paper presents a comprehensive comparative survey of TensorFlow and PyTorch, the two leading deep learning.
In this paper we will explore TensorFlow.js, a JavaScript library for using machine learning in browsers. The main goal is to compare and classify different ...
The applications and widespread use of machine learning algorithms have made a significant change in the way we perceive computer vision problems. With the introduction of deep learning into the field of image classification, the dynamics of real-time object detection have faced a great impact.
TensorFlow supports a variety of applications, with a focus on training and inference on deep neural net- works. Several Google services use TensorFlow
Among the typical commercial use cases are Natural Language Processing (NLP) and computer vision. . A couple of examples are Quick, Draw! (Google, 2020) or ...
TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.
We then compare Ten-sorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and industry. Index Terms—Artificial Intelligence, Machine Learning, Neu-ral Networks, Distributed Computing, Open source software, Software packages
