#!/usr/bin/env python
# coding: utf-8

# In[1]:


# %load /Users/facai/Study/book_notes/preconfig.py
get_ipython().run_line_magic('matplotlib', 'inline')

#import matplotlib.pyplot as plt
#import seaborn as sns
#sns.set(color_codes=True)
#sns.set(font='SimHei', font_scale=2.5)
#plt.rcParams['axes.grid'] = False

#import numpy as np

#import pandas as pd
#pd.options.display.max_rows = 20

#import sklearn

#import itertools


# Chapter 12 Application
# =============

# ### 12.1 Large Scale Deep Learning
# 
# + Fast CPU Implementations
#   - specializated numerical computation routines
#   - optimizing data struture (avoid cache misses)
#   - using vector instructions
# + GPU Implementations
# + Model Compression
#   fit bigger model -> populate samples -> train a smaller model
# + Dynamic Structure: 
#   accelerating inference => cascade of classifiers
# + lower precision to accelerate train/inference
# 
# 
# ### 12.2 Computer Vision
# 
# + Preprocessing:
#   - Standardization
#   - Contrast Normalization
#     1. global contrast normalization
#     2. local contrast normalization
#   - Dataset Augmentation
#   
# 
# ### 12.3 Speech Recognition
# 
# + LSTM RNN
# + CTC framework
#   
# 
# ### 12.4 Natural Language Processing
# 
# + n-grams
# + Neural Language Models
#   - word embeddings
#   
# 
# ### 12.5 Other Applications
# 
# + Recommender Systems
#   - reinforcement learning
# + Knowledge Representation, Reasoning and Question Answering

# In[ ]: