AI人工智能训练营

START:
February 17, 2018
DURATION:
6周
PRICE:
$2400

 

AI人工智能训练营

各位理工科技术型人才, AI是什么, 怎样快速入门领先他人一步?

各个领域的程序员门, 要不要转型AI, 怎么学?

想要自己做Project, 学习Tensorflow, 打造成全能型职场炙手可热的人才?

人工智能是一门复杂的学科, 涉及面广, 知识点多. 需要诸多综合能力, 例如编程, 大数据, 云计算, 机器学习, 自然语言处理, 神经网络等.

针对人工智能的就业前景和市场调研, 为了适应北美的职场需求, 我们精心设计了这门AI人工智能课程.


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


课程周期:

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, Machine Learning), 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Deep Learning (Neural Network, deep neutral network, convolution neural network, 调参技巧, RNN等)

用Tensorflow实战Facial Recognition, 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 (Syllabus):

Modular 1 – Machine Learning

Class 1 Regression

  1. Basic concept of Regression
  2. Bias-Variance trade off
  3. Underfitting vs. Overfitting
  4. Linear regression analytical solution
  5. Regularization:

Lasso

Ridge

Elastic-Net

Pros and cons of L1 and L2 regularization

  1. Advanced techniques in regression

Gradient Descendent

Coordinated Descendent

Stochastic Gradient Descendent

Random sample consensus (RANSAC)

 

Class 2 Classification

  1. Evaluation Methods of classification
  2. Basic classification model: logistic regression, decision tree
  3. Classification Types (how binary and multi-class works)
  4. Ensemble model method:

Bagging

Boosting

Stacking

 

Class 3 Dimension Reduction

  1. Dimension reduction overview
  2. Dimension reduction methods

Randomized Projection

Principal Component Analysis

PCA Calculation

Randomized PCA

Sparse PCA

  1. Manifold learning
  2. Multidimensional Scaling

MDS

Isomap

Class 4 Clustering

  1. Unsupervised learning introduction
  2. Clustering methods & techniques

K-mean Algorithm

Hierarchical Clustering Algorithm

DBSCAN algorithm

Outlier and anomaly detection

 

Modular 2 – Neural Network & Deep Learning

Class 1

Neural network basic (which maybe duplicate with NLP part)

  • Introduction to neural network, include some basic concept like neuron, weights, bias, activation function.
  •  Forward propagation for inference
  •  Training algorithm: backpropagation (use 1 hidden layer neural network with binary output as example)

Deep neural network

  • Fully connected layer
  • Shows how to use DNN for MNIST digit recognition problem

Convolutional neural network

  • Motivation: why use CNN in computer vision problem: position invariance.
  • Convolution
  • Intro to convolutional layer + pooling layer
  •  Revisit MNIST problem and show how to use CNN to improve it. #params reduced.

Class 2

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

Introduction to famous vision problems and corresponding networks

  •      Image classification: Alexnet
  •      Object detection: R-CNN
  •      Image segmentation: U-Net

Useful technicals for neural network training

  •      Performance: try different network structure, different number of layers and different number of hidden units in each layer
  •      Converge:  sensitive to learning rate
  •      Speed up training: Stochastic Gradient Descent, Momentum
  •      Gradient vanishing problem: Batch Normalization

Recurrent Neural Network for video learning

  •      RNN basic (this maybe duplicate with NLP part)
  •      Use example to show how to use RNN for video analysis.

Reinforcement learning

  •      Deep Q-learning
  •      Playing Atari game by DeepMind

(Brief introduction installation tensorflow)

Class 3 – Tensorflow and Facial Recognition

Brief introduction to Tensorflow: Tensor, operator concept.

Shows one small network 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 each person for training. Target is to label the left 40 images (1 per person) to the right person.

Face recognition is widely used technologies, such as photo softwares, surveillance.

Class 4

Finish the basic version for the Face recognition.

Improve network:

  •      Try different learning rate
  •      Add more layer
  •      Add more neurons for each layer
  •      Compare DNN and CNN
  •      Try different optimizer

 

Modular 3 – Deep Learning and Natural Language Processing

Class 1

  • Intro to NLP and Deep Learning
  • what is NLP?
  • NLP difficulty level
  • Industry applications
  • Deep neural network for NLP
  • Phonology and Morphology
  • Syntax and Semantics
  • Question Answering

Class 2

  • Simple Word Vector representations: word2vec, GloVe
  • Vector(discrete) Representation
  • Problem with discrete representation
  • Cooccurence Matrix
  • Main idea of word2vec
  • Main idea of Glove

Class 3

  • Complicated Models for NLP
  • Recurrent Neural Networks
  • Alignment
  • Gated Recurrent Units
  • Long-short-term-memories (LSTMs)

Class 4

  • TensorFlow for NLP
  • A recap of tensorflow
  • NLP specific tensorflow
  • Build a tensorflow based chatbot from scratch

价格:$2400

开始日期: Feb 17