2017新人教育スケジュール（機械学習関連）

# １週目

## 6/12 (配属初日) :v:

#### 業務紹介

#### githubの登録

#### 機械学習
* AIとMLとDLの関係性
* 頻度統計 v.s. ベイズ統計
    * 最尤推定 v.s. ベイズ推定
* 過学習(overfitting)
    * 対策: 正則化、dropout
* パラメータチューニング
    * train-validation-test-split
* 教師有モデル
    * 分類モデル
        * NN (Neural Network)
            * gradient descent
            * back propagation
        * SVM (Support Vector Machine)
            * まだ知らなくて良い：kernel trick
        * DT (Decision Tree)
            * まだ知らなくて良い：informational gain
        * RF (Random Forest)
            * 参考：bootstrap resampling

#### Python

* jupyter notebookの紹介

```python:test.ipynb
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0,10,0.1)
y = np.sin(x)
plt.plot(x,y)
```

## 6/13 :v:

#### scikit-learn

* sklearnのワークフロー
    1. model = Model()
    2. model.fit()
    3. model.predict()

## 6/14 :v:

#### tensorflow

* tensorflowのワークフロー
* インストール w/ anaconda @ windows 10

#### Deep Neural Network

* DNNの基本的な話
* activations
    * tanh
    * relu
    * lrelu
    * prelu
    * elu
* optimizer
    * sgd
    * momentum
    * adagrad/ adadelta/ rmsprop
    * adam
* initializer
    * xavier init.
    * he init.

## 6/15 :v:

#### keras

* kerasのワークフロー

#### Autoencoder

* denoising AE
* sparse AE
* variational AE (またあとで説明

#### Anomaly Detection

## 6/16 :v:

#### Convolutional Neural Network

* convolution
* pooling

#### Transfer Learning

#### Normalization

* batch normalization
* weight normalization
* layer normalizaiton

#### Image Recognition

* LeNet
* AlexNet
* ZFNet
* VGGNet
* NIN
* GoogLeNet
* ResNet

# ２週目

## 6/19 :v:

#### Keras

* Practice: CNN

## 6/20 :v:

#### Keras

* Practice: Transfer Learning

## 6/21 :v:

#### Keras

* Practice: Transfer Learning

#### Model Compression

* Deep Compression
* Depse-wise Conv. 
    * Xception
    * Mobilenet

## 6/22 :v:

#### Keras

* Practice: Transfer Learning

#### Similarity Learning

* Siamese Network

## 6/23 :v:

#### Object Detection

* R-CNN
* SPPNet
* Fast R-CNN
* Faster R-CNN
* Overfeat
* AttentionNet
* SSD
* YOLO

# ３週目

## 6/26 :v:

#### Semantic Segmentation

* Deconvolution
* FCN
* Mask R-CNN

#### Recurrent Neural Networks

* LSTM
* GRU
* Bi-RNN

#### Neural Machine Translation

* Seq2Seq
* Attention
* Slicenet

#### Image Caption Generation

#### Keras

* Practice: RNN-LSTM

## 6/27 :v:

#### Natural Language Processing

* word2vec
    * Skipgram
    * CBoW(Continuous Bag of Words)
    * Glove
* sentence2vec
    * BoW
    * Recursive Neural Network
    * Glove

## 6/28 :v:

#### Generative Models

* GAN: Generative Adversarial Networks
    * DCGAN
* Conditional GAN
    * Pix2Pix

#### Adversarial Examples
    
## 6/29 :v:

#### Generative Models

* VAE: Variational Autoencoder
    * DRAW
* Pixel RNN/ Pixel CNN
    * Wavenet
    * Bytenet
    
## 6/30 :v:

#### Understanding CNN
* Maximally Activating Patches
* Occlusion Maps
* Saliency Maps
* Features Inversion via backprop

#### Neural Style Transfer

* Deep Dream
* Neural Art

## 7/3 :v:

#### Reinforcement Learning

* MDP
* Policy Gradient
    * AlphaGo

---

## 7/12 以降

#### Reinforcement Learning

* Q-Learning
    * DQN
* Actor-Critic
    * A3C

#### NN w/ External Memory

* Neural Turing Machine
* Differential Neural Computer

#### Meta Learning

* Evolutional Strategy

#### One-Shot Learning

* Matching Networks

#### Semi-Supervised Learning

* GAN / VAE / Virtual Adversarial Training

#### Self-Driving Cars

* NVIDIA Model
* Localization
* Sensor Fusion
* Kalman filter
* PID Control
* SLAM
* Path Planning

#### Robotics

* Perception / Decision / Action