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Artificial Neural Networks, Machine Learning, Deep Thinking培训

 
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       坚持小班授课,为保证培训效果,增加互动环节,每期人数限3到5人。
   上课时间和地点
上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【广州分部】:广粮大厦 【西安分部】:协同大厦
近开课时间(周末班/连续班/晚班):2019年1月26日
   实验设备
     ☆资深工程师授课
        
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   质量保障

        1、培训过程中,如有部分内容理解不透或消化不好,可免费在以后培训班中重听;
        2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供半年的技术支持。
        3、培训合格学员可享受免费推荐就业机会。

课程大纲
 

DAY 1 - ARTIFICIAL NEURAL NETWORKS

Introduction and ANN Structure.

Biological neurons and artificial neurons.
Model of an ANN.
Activation functions used in ANNs.
Typical classes of network architectures .
Mathematical Foundations and Learning mechanisms.

Re-visiting vector and matrix algebra.
State-space concepts.
Concepts of optimization.
Error-correction learning.
Memory-based learning.
Hebbian learning.
Competitive learning.
Single layer perceptrons.

Structure and learning of perceptrons.
Pattern classifier - introduction and Bayes' classifiers.
Perceptron as a pattern classifier.
Perceptron convergence.
Limitations of a perceptrons.
Feedforward ANN.

Structures of Multi-layer feedforward networks.
Back propagation algorithm.
Back propagation - training and convergence.
Functional approximation with back propagation.
Practical and design issues of back propagation learning.
Radial Basis Function Networks.

Pattern separability and interpolation.
Regularization Theory.
Regularization and RBF networks.
RBF network design and training.
Approximation properties of RBF.
Competitive Learning and Self organizing ANN.

General clustering procedures.
Learning Vector Quantization (LVQ).
Competitive learning algorithms and architectures.
Self organizing feature maps.
Properties of feature maps.
Fuzzy Neural Networks.

Neuro-fuzzy systems.
Background of fuzzy sets and logic.
Design of fuzzy stems.
Design of fuzzy ANNs.
Applications

A few examples of Neural Network applications, their advantages and problems will be discussed.
DAY -2 MACHINE LEARNING

The PAC Learning Framework
Guarantees for finite hypothesis set – consistent case
Guarantees for finite hypothesis set – inconsistent case
Generalities
Deterministic cv. Stochastic scenarios
Bayes error noise
Estimation and approximation errors
Model selection
Radmeacher Complexity and VC – Dimension
Bias - Variance tradeoff
Regularisation
Over-fitting
Validation
Support Vector Machines
Kriging (Gaussian Process regression)
PCA and Kernel PCA
Self Organisation Maps (SOM)
Kernel induced vector space
Mercer Kernels and Kernel - induced similarity metrics
Reinforcement Learning
DAY 3 - DEEP LEARNING

This will be taught in relation to the topics covered on Day 1 and Day 2

Logistic and Softmax Regression
Sparse Autoencoders
Vectorization, PCA and Whitening
Self-Taught Learning
Deep Networks
Linear Decoders
Convolution and Pooling
Sparse Coding
Independent Component Analysis
Canonical Correlation Analysis
Demos and Applications

 
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