Juan Rivera Libros

Cientos de remedios tradicionales para aliviar molestias y enfermedades, mejorar tu salud y tu aspecto.«Soy un doctor formado académicamente de acuerdo con los cánones de la medicina tradicional, pero con una mente abierta y un deseo continuo de aprender sobre terapias naturales que podrían mejorar la calidad de vida de mis pacientes y seguidores.»Doctor Juan RiveraSeguro que tienes muchos de estos ingredientes en la cocina -o los has visto en el mercado- y no sabías que: la canela controla el azúcar y mejora la digestión, el hinojo baja la presión arterial y sirve para lavar los ojos, la manzanilla previene el insomnio y ayuda a cicatrizar, el orégano fortalece el sistema inmunológico y contrarresta el envejecimiento.Descubre cómo evitar los ronquidos con una pelota de tenis, combatir los ataques de asma con café, eliminar el mal olor de los pies con té negro y muchos secretos más.Con su estilo ameno, el doctor Juan Rivera revela muchos remedios naturales, nos cuenta sus orígenes, nos explica por qué funcionan con los estudios científicos que avalan su eficacia y nos brinda consejos e instrucciones precisas para beneficiarnos de ellos.

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. You will: Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis