Tag: TensorFlow

Build GANs and Diffusion Models with TensorFlow and PyTorch


Free Download Build GANs and Diffusion Models with TensorFlow and PyTorch
Released 9/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Advanced | Genre: eLearning | Language: English + srt | Duration: 2h 22m | Size: 1.8 GB
If you’re looking for a crash course in generative modeling, this course was made for you. Generative adversarial networks (GANs) and diffusion models are some of the most important components of machine learning infrastructure. Join instructor Janani Ravi to find out more about how to get started building GANs with both dense neural as well as deep convolutional networks. Javani shows you the basics of how to train a deep convolutional GAN on multichannel images. Along the way, she gives you tips on how to get up and running with GANs using TensorFlow and diffusion models using PyTorch.

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Learning TensorFlow.js Powerful Machine Learning in JavaScript


Free Download Learning TensorFlow.js: Powerful Machine Learning in JavaScript by Gant Laborde
English | June 15, 2021 | ISBN: 1492090794 | 338 pages | MOBI | 6.35 Mb
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learning and the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.

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TensorFlow Neural Networks and Working with Tables


Free Download TensorFlow: Neural Networks and Working with Tables
Duration: 43m | .MP4 1280×720, 30 fps(r) | AAC, 48000 Hz, 2ch | 142 MB
Level: Intermediate | Genre: eLearning | Language: English
TensorFlow 2.0 is quickly becoming one of the most popular deep learning frameworks and a must-have skill in your artificial intelligence toolkit. Using a hands-on approach, instructor Jonathan Fernandes covers foundational skills for deep learning using TensorFlow 2.0, from creating single and multi-layer networks, to training a network, and using it to make predictions. He also covers loss functions, optimizers, and some of the data APIs unique to TensorFlow.
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