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| date | title | contents | |
|---|---|---|---|
| 1 | 2017/4/11 | Introduction | Deep Learningとは/人工知能技術の歴史、社会への影響/講義全体像と注意事項/ 全員挨拶 |
| 2 | 2017/4/18 | Machine Learning 1 | Pythonと線形代数，行列・テンソル，数式と実装のブリッジ |
| 3 | 2017/4/25 | Machine Learning 2 | k-NN, Logistic Regression, Softmax, train/dev/testデータセット,学習プロセス |
| 4 | 2017/5/2 | Perceptron + Feed Forward Network, Gradient Descent | Gradient Descent, MLP, Logistic Regression, Softmax |
| 5 | 2017/5/9 | Gradient Descent, Stochastic Gradient Descent, Optimizers | TensorFlow基礎、各種Optimizers、重みの初期化など |
| 6 | 2017/5/16 | Autoencoders | Deep Learning、特徴抽出、Autoencoder概要、dA, SdA, Sparse Coding, GPU |
| 7 | 2017/5/23 | Convolutional Neural Networks(CNN) | CNN基礎、畳込み、プーリング |
| 8 | 2017/6/6 | Convolutional Neural Networks(CNN) 2 | 画像処理、前処理、Data Augmentation、Batch Normalization、可視化、最新動向 |
| 9 | 2017/6/13 | RNN Basics | 系列データ, RNN |
| 10 | 2017/6/20 | RNN and NLP | Embedding, Projection, Word2vec, sequence-to-sequence |
| 11 | 2017/6/27 | RNN, NLP, Image Processing | Attention, Image caption |
| 12 | 2017/7/4 | Summary and Advanced Topics | Advanced Topics |