notebook.community



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# %load /Users/facai/Study/book_notes/preconfig.py
%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



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