Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python

Chapter 1: Introduction to Neural Network Intelligence1.1 Installation1.2 Trial, search space, experiment1.3 Finding maxima of multivariate function1.4 Interacting with NNI
Chapter 2: Hyper-Parameter Tuning2.1 Preparing a model for hyper-parameter tuning2.2 Running experiment2.3 Interpreting results2.4 Debugging
Chapter 3: Hyper-Parameter Tuners
Chapter 4: Neural Architecture Search: Multi-trial4.1 Constructing a search space4.2 Running architecture search4.3 Exploration strategies4.4 Comparing exploration strategies
Chapter 5: Neural Architecture Search: One-shot5.1 What is one-shot NAS?5.2 ENAS5.3 DARTS
Chapter 6: Model Compression6.1 What is model compression?6.2 Compressing your model6.3 Pruning6.4 Quantization
Chapter 7: Advanced NNI

第一章：神經網絡智能介紹
1.1 安裝
1.2 試驗、搜索空間、實驗
1.3 尋找多變量函數的極大值
1.4 與 NNI 互動

第二章：超參數調整
2.1 為超參數調整準備模型
2.2 執行實驗
2.3 解讀結果
2.4 調試

第三章：超參數調整器

第四章：神經架構搜索：多試驗
4.1 構建搜索空間
4.2 執行架構搜索
4.3 探索策略
4.4 比較探索策略

第五章：神經架構搜索：一次性
5.1 什麼是一次性 NAS？
5.2 ENAS
5.3 DARTS

第六章：模型壓縮
6.1 什麼是模型壓縮？
6.2 壓縮您的模型
6.3 剪枝
6.4 量化

第七章：高級 NNI

Ivan Gridin is a machine learning expert from Moscow who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and analysis of predictive time series models. Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He has published books on genetic algorithms and time series analysis.

伊凡·格里丁（Ivan Gridin）是一位來自莫斯科的機器學習專家，他在分散式高負載系統上工作，並實踐了不同的機器學習方法。他的研究主要領域之一是預測時間序列模型的設計和分析。伊凡在概率論、隨機過程理論、時間序列分析、機器學習、深度學習和優化方面具有基礎數學技能。他曾出版過關於遺傳算法和時間序列分析的書籍。