免费一看一级欧美-免费一区二区三区免费视频-免费伊人-免费影片-99精品网-99精品小视频

      ☆ 1、如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
      ☆ 2、在課程結束之后,授課老師會留給學員手機和E-mail,免費提供半年的課程技術支持，以便保證培訓后的繼續消化；
      ☆3、合格的學員可享受免費推薦就業機會。
      ☆4、合格學員免費頒發相關工程師等資格證書，提升您的職業資質。

CDAY 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