用TensorFlow 2.0构建机器学习解决方案(路径)

谷歌于2019年10月发布了TensorFlow 2.0，该版本使用动态图，对Python更加友好。有多项更改可以确保删除冗余的API，并与Python运行时和急切执行更好地集成。

了解开始使用Tensorflow 2.0所需的一切。Building Machine Learning Solutions with TensorFlow 2.0 (Path)
通过学习如何为Tensorflow 2.0设计数据管道和实施超参数调整，加深您对TensorFlow的理解。
了解如何使用Keras构建机器学习工作流，并使用时间序列数据生成高性能的预测和预报。

Janani Ravi(等人)|时长:13小时00分钟|视频:H264 1280×720 |音频:AAC 48 kHz 2ch | 大小解压后2.17 GB |语言：英语+中英文字幕（云桥网络 机译）

这套课程包含：

A1。TensorFlow 2.0入门(Janani Ravi，2020)
B1。用TensorFlow 2.0设计数据管道(蔡斯·韩德，2020年)
B3。用TensorFlow.js构建机器学习解决方案(Abhishek Kumar，2020)
C1。用Keras TensorFlow 2.0构建机器学习工作流(Janani Ravi，2020)
C2。用TensorFlow 2.0实现时间序列分析、预测和预报(大通韩德，2020年)









Janani Ravi (et al.) | Duration: 13h 00m | Video: H264 1280×720 | Audio: AAC 48 kHz 2ch | 2,02 GB | Language: English

Google released TensorFlow 2.0 in October 2019 which uses the dynamic graph and is more Python friendly. There are multiple changes to ensure removal of redundant APIs and better integration with Python runtime and Eager Execution.

• Learn everything you need to know to get started with Tensorflow 2.0.
• Step up your TensorFlow understanding by learning how to design data pipelines and implement hyperparameter tuning for Tensorflow 2.0.
• Learn how to build a machine learning workflow with Keras and work with time series data to generate high performing forecasts and predictions.

Courses in this path

A1. Getting Started with TensorFlow 2.0 (Janani Ravi, 2020)
B1. Designing Data Pipelines with TensorFlow 2.0 (Chase DeHan, 2020)
B3. Building Machine Learning Solutions with TensorFlow.js (Abhishek Kumar, 2020)
C1. Build a Machine Learning Workflow with Keras TensorFlow 2.0 (Janani Ravi, 2020)
C2. Implement Time Series Analysis, Forecasting and Prediction with TensorFlow 2.0 (Chase DeHan, 2020)

云桥网络 为三维动画制作，游戏开发员、影视特效师等CG艺术家提供视频教程素材资源！