久违到哭，丹·布朗终于带着探讨AI的新作杀回来了！

      去年底，久违的丹·布朗大叔终于带着新作《Origin》回归了，距离上一本小说《地狱》隔了四年！而距离红遍大江南北的《达芬奇密码》，已经过去了近15年！！！！！！

小编的最爱是《天使与魔鬼》，中文版看过两遍以后，英文版和电影又各刷了一遍。话说刷英文版那段时间也经历着失眠的困扰，结果刷着刷着，养成了刷15分钟秒睡的好习惯...... 在此跑题推荐有失眠困扰的孩子们，都可以尝试一下丹布朗的安眠药。

作为忠实书迷的小编，当然是第一时间，买了本有声书，18个小时9分钟的时长。正好那段时间有些失眠，每天睡前开启15分钟有声书睡眠模式，即使小说情节越来越精彩，也抵挡不住我洪荒的睡意...... 就酱紫，每天睡前15分钟加一趟去加拿大的长途开车，把这部大部头听完了......

还是熟悉的丹·布朗风格，融合了密码学、科技、宗教、历史、艺术、建筑等元素...... 最让人眼前一亮的是，丹叔竟然抱起了人工智能的大腿。丹叔表示，近期一直在密切关注人工智能技术方面的进展。丹叔对人工智能的兴趣在新书中得到了很好的呈现，书中人工智能Winston简直聪明机智到令人发指......

还是熟悉的罗伯特·兰登配方，最大的不同大概是，老当益壮but风流倜傥、几乎每部都要换个兰登女郎的哈佛教授，这部没和女主发展爱情线了，毕竟女主是未来西班牙王后......

(汤姆汉克斯饰演了电影《天使与魔鬼》、《达芬奇密码》、《但丁密码》中的罗伯特·兰登)

据说，丹叔还会将兰登教授的故事继续写下去，而且下一部的探险目的地有可能是中国。毕竟在新书中，丹叔可是出乎意料地小舔了一下wuli清华大学。书中一名西班牙王室的公关，就在清华大学留过学。

《Origin》在亚马逊上的评分是4星，评价正负参半。总体来说，小编觉得这部作品值得3.8-4分的评价，想练英语听力和治疗失眠的，可以买有声书来听，科科。中文版据说将在今年春天上架，大家拭目以待！

话说回来，书迷们都在翘首期盼新作的这四年里，丹叔到底在干嘛呢？也许，去学人工智能了吧？

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理工科或者计算机Computer Science专业, 数学统计专业, 计算机编程爱好者

如果Python背景比较弱, 可以先参加我们的Python基础入门课程

课程周期

2月17日起，全长6周, 每周六周日2小时课程

课程特色

三大模块: 机器学习, 深度学习与神经网络, 案例项目实践

双语教学: 机器学习Machine Learning部分全程由英文教学, 方便学员未来求职时对答如流. 深度学习与神经网络和项目实践部分才用中英双语

三大名师: Peter(USC Information Institute Post Doc, MachineLearning), Carol (Google工程师, 精通Tensorflow), Eric(Google工程师, AI专家)

前沿技术: 课程设计多项AI领域必备前沿技术, 包括全面系统的Machine Learning知识讲解梳理, 神经网络与深度学习从入门到实践,Tensorflow实战入门, 人脸识别项目, NLP自然语言处理实战项目

实战演练: 课程内容基于实战项目, 边学习边练习 项目一: Facial Recogniztion 项目二:Natural Language Processing  (详见syllabus)

课程收获

• 全面系统了解Machine Learning(Regression, Classification,Dimension Reduction, Clustering)

• 了解Neutral Network与DeepLearning (Neural Network, deep neutral network, convolution neural network, 调参技巧, RNN等)

• 用Tensorflow实战FacialRecognition, 并且尝试改进与调参

• 学习和掌握深度学习与NLP自然语言处理 (NLP概念与基础, word2vec, GloVe, 复杂NLP模型)

• 用Tensorflow实战NLP项目

求职助力

• 课程项目适用于求职简历, 增强简历效果

• 加入Data Application Lab海量求职内推网络

• 简历与面试助攻(详情请咨询课程老师)

课程内容

Modular 1 – Machine Learning

Class 1 Regression

1. Basic concept of Regression

2. Bias-Variance tradeoff

3. Underfitting vs.Overfitting

4. Linear regressionanalytical solution

5. Regularization: Lasso，Ridge，Elastic-Net，Pros and cons of L1and L2 regularization

6. Advanced techniquesin regression，Gradient Descendent，CoordinatedDescendent，Stochastic GradientDescendent，Random sampleconsensus (RANSAC)

Class 2 Classification

1. Evaluation Methodsof classification

2. Basicclassification model: logistic regression, decision tree

3. ClassificationTypes (how binary and multi-class works)

4. Ensemble modelmethod: Bagging，Boosting，Stacking

Class 3 DimensionReduction

1. Dimension reduction overview

2. Dimension reduction methods：Randomized Projection，Principal Component Analysis，PCA Calculation，Randomized PCA，Sparse PCA

3. Manifold learning

4. MultidimensionalScaling：MDS，Isomap

Class 4 Clustering

1. Unsupervisedlearning introduction

2. Clustering methods& techniques：K-mean Algorithm，HierarchicalClustering Algorithm，DBSCAN algorithm，Outlierand anomaly detection

Modular 2 – NeuralNetwork & Deep Learning

Class 1 

Neuralnetwork basic (which maybe duplicate with NLP part)

• Introduction toneural network, include some basic concept like neuron, weights, bias,activation function.

• Forward propagation for inference

• Training algorithm: backpropagation (use 1hidden layer neural network with binary output as example)

Deep neural network

• Fully connectedlayer

• Shows how to useDNN for MNIST digit recognition problem

Convolutionalneural network

• Motivation: why useCNN in computer vision problem: position invariance.

• Convolution

• Intro toconvolutional layer + pooling layer

• Revisit MNIST problem and show how to use CNNto improve it. #params reduced.

Class 2 Short recap to fully connected layer, convolutional layer and pooling layer.

Introduction tofamous vision problems and corresponding networks

• Imageclassification: Alexnet

• Object detection:R-CNN

• Image segmentation:U-Net

Useful technicalsfor neural network training

• Performance: trydifferent network structure, different number of layers and different number ofhidden units in each layer

• Converge:  sensitive to learning rate

• Speed up training:Stochastic Gradient Descent, Momentum

• Gradient vanishingproblem: Batch Normalization

Recurrent NeuralNetwork for video learning

• RNN basic (thismaybe duplicate with NLP part)

• Use example to showhow to use RNN for video analysis.

Reinforcementlearning

• Deep Q-learning

• Playing Atari gameby DeepMind (Brief introductioninstallation tensorflow)

Class 3 –Tensorflow and Facial Recognition

Brief introductionto Tensorflow: Tensor, operator concept.

Shows one smallnetwork structure and shows how to write it in Tensorflow.

Lab problem: 

Face recognition:given face images for 40 person, each have 10 images, use 9 images of eachperson for training. Target is to label the left 40 images (1 per person) tothe right person.

Face recognition iswidely used technologies, such as photo softwares, surveillance.

Class 4

Finish the basic versionfor the Face recognition.

Improve network:

• Try differentlearning rate

• Add more layer

• Add more neuronsfor each layer

• Compare DNN and CNN

• Try differentoptimizer

Modular 3 – DeepLearning and Natural Language Processing

Class 1

• Intro to NLP andDeep Learning

• what is NLP?

• NLP difficultylevel

• Industryapplications

• Deep neural networkfor NLP

• Phonology andMorphology

• Syntax andSemantics

• Question Answering

Class 2

• Simple Word Vectorrepresentations: word2vec, GloVe

• Vector(discrete)Representation

• Problem withdiscrete representation

• Cooccurence Matrix

• Main idea ofword2vec

• Main idea of Glove

Class 3

• Complicated Modelsfor NLP

• Recurrent NeuralNetworks

• Alignment

• Gated RecurrentUnits

• Long-short-term-memories(LSTMs)

Class 4

TensorFlow for NLP

A recap oftensorflow

NLP specifictensorflow

Build a tensorflowbased chatbot from scratch

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