久久综合色88_欧美激情国产日韩精品一区18_午夜精品一区二区三区在线观看 _自拍日韩亚洲一区在线

        1、培訓(xùn)過程中，如有部分內(nèi)容理解不透或消化不好，可免費(fèi)在以后培訓(xùn)班中重聽；
        2、培訓(xùn)結(jié)束后,授課老師留給學(xué)員聯(lián)系方式,保障培訓(xùn)效果,免費(fèi)提供課后技術(shù)支持。
        3、培訓(xùn)合格學(xué)員可享受免費(fèi)推薦就業(yè)機(jī)會。

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