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课程大纲
介绍
- Chainer 与 Caffe 与 Torch
- Chainer 功能和元件概述
开始
- 了解训练器结构
- 安装 Chainer、CuPy 和 NumPy
- 在变数上定义函数
在 Chainer 中训练 Neural Networks
- 构造计算图
- 运行 MNIST 资料集示例
- 使用优化器更新参数
- 处理图像以评估结果
在 Chainer 中使用 GPU
- 实现递回神经网路
- 使用多个 GPU 进行并行化
实现其他神经网路模型
- 定义 RNN 模型和运行范例
- 使用 Deep Convolutional GAN 生成图像
- 运行 Reinforcement Learning 范例
故障排除
总结和结论
要求
- 对人工神经网路的理解
- 熟悉深度学习框架（Caffe、Torch 等）
- Python 程式设计经验
观众
- AI 研究人员
- 开发人员
14 小时
客户评论 (5)
Hunter非常出色，非常有吸引力，知识渊博且平易近人。表现非常出色。
Rick Johnson - Laramie County Community College
课程 - Artificial Intelligence (AI) Overview
机器翻译
培训师是该领域的专业人士，能够出色地将理论与实际应用相结合
Fahad Malalla - Tatweer Petroleum
课程 - Applied AI from Scratch in Python
机器翻译
I liked the new insights in deep machine learning.
Josip Arneric
课程 - Neural Network in R
机器翻译
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
课程 - Introduction to the use of neural networks
机器翻译
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
课程 - Artificial Neural Networks, Machine Learning, Deep Thinking
机器翻译
