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課程簡介
介紹
- Chainer vs Caffe vs Torch
- Chainer 功能和元件概述
開始
- 瞭解培訓師結構
- 安裝 Chainer、CuPy 和 NumPy
- 在變數上定義函數
在 Chainer 中培訓 Neural Networks
- 構建計算圖
- 運行 MNIST 資料集示例
- 使用優化器更新參數
- 處理圖像以評估結果
在 Chainer 中使用 GPU
- 實現遞迴神經網路
- 使用多個 GPU 進行並行化
實現其他神經網路模型
- 定義 RNN 模型和運行範例
- 使用深度卷積 GAN 生成圖像
- 運行 Reinforcement Learning 範例
故障排除
總結和結論
最低要求
- 對人工神經網路的理解
- 熟悉深度學習框架(Caffe、Torch 等)
- Python程式設計經驗
觀眾
- 人工智慧研究人員
- 開發人員
14 時間:
客戶評論 (4)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
Very flexible
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)