2月24日
《AI人工智能训练营》
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2月24日开课 | 数据应用学院 AI人工智能训练营火热报名中
数据应用学院(Data Application Lab)专注于数据, 开办3年来已向全球知名企业输送数百Data Scientists, 更有不计其数的Data Analysts以及Engineers, Business Analysts。多年的钻研和专一打造了独一无二教学方法和求职经验。一直被模仿, 从未被超越。已被多加北美英文科技媒体列为Top 10 North American Data Bootcamp。学员遍布全球, 至今时常还有来自欧洲, 亚太等地的申请者报名。
”适合学员背景
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
发送简历至DataEngineer@DataAppLab.com
点击“阅读原文”直接报名
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课程周期
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
Basic concept of Regression
Bias-Variance tradeoff
Underfitting vs.Overfitting
Linear regressionanalytical solution
Regularization: Lasso，Ridge，Elastic-Net，Pros and cons of L1and L2 regularization
Advanced techniquesin regression，Gradient Descendent，CoordinatedDescendent，Stochastic GradientDescendent，Random sampleconsensus (RANSAC)
Class 2 Classification
Evaluation Methodsof classification
Basicclassification model: logistic regression, decision tree
ClassificationTypes (how binary and multi-class works)
Ensemble modelmethod: Bagging，Boosting，Stacking
Class 3 DimensionReduction
Dimension reduction overview
Dimension reduction methods：Randomized Projection，Principal Component Analysis，PCA Calculation，Randomized PCA，Sparse PCA
Manifold learning
MultidimensionalScaling：MDS，Isomap
Class 4 Clustering
Unsupervisedlearning introduction
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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点击“阅读原文”直接报名
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