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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


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