目录 概 相关工作 主要内容 引理1 定理1 定理2 A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern 概 作者关于Loss Surface的情况做了一个理论分析, 即证明足够大的神经网络能够逼近所有的低维损失patterns. 相关工作 loss landscape 的提及. 文中多处用到了universal approximators. 主要内容 引理1 \(\mathcal{F}\)定义了…
About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…
一.Training of a Single-Layer Neural Network 1 Delta Rule Consider a single-layer neural network, as shown in Figure 2-11. In the figure, d i is the correct output of the output node i. Long story short, the delta rule adjusts the weight as the follow…
1, 数据集简介    notMNIST, 看名字就知道,跟MNIST脱不了干系,其实就是升级版的MNIST,含有 A-J 10个类别的艺术印刷体字符,字符的形状各异,噪声更多,难度比 MNIST 要大,图片大小为28x28,灰度,与MNIST类似. 2,数据处理    下载的数据集解压之后是 A-J 10个文件夹,里面存放每个类别的 png 图片,并非一般的按二进制存储的数据集,所以先要将数据整理成便于读取的文件格式,按照Udacity deep learning 的教程,数据处理包括了 下载…
An end to end implementation of a Machine Learning pipeline SPANDAN MADAN Visual Computing Group, Harvard University Computer Science and Artificial Intelligence Laboratory, MIT   Link to Github Repo   Section 1. Introduction Background In the fall o…
def get_model(width, height, classes=40): # TODO, modify model # Building 'VGG Network' network = input_data(shape=[None, width, height, 1]) # if RGB, 224,224,3 network = conv_2d(network, 64, 3, activation='relu') #network = conv_2d(network, 64, 3, a…
network = tflearn.input_data(shape=[None, max_len], name='input') network = tflearn.embedding(network, input_dim=volcab_size, output_dim=32) network = conv_1d(network, 64, 3, activation='relu', regularizer="L2") network = max_pool_1d(network, 2)…
一些先进的网络结构: # https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py # -*- coding: utf-8 -*- """ Deep Neural Network for MNIST dataset classification task using a highway network References: Links: [MNIST Dataset] http…
lstm.py # -*- coding: utf-8 -*- """ Simple example using LSTM recurrent neural network to classify IMDB sentiment dataset. References: - Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1…
from sklearn.feature_extraction.text import CountVectorizer import os from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import train_test_split from sklearn import metrics import matplotlib.pyplot as plt import numpy as np from…