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            曙海教育集團
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            Artificial Neural Networks, Machine Learning, Deep Thinking培訓

             
               班級規模及環境--熱線:4008699035 手機:15921673576( 微信同號)
                   每期人數限3到5人。
               上課時間和地點
            上課地點:【上?!浚和瑵髮W(滬西)/新城金郡商務樓(11號線白銀路站) 【深圳分部】:電影大廈(地鐵一號線大劇院站)/深圳大學成教院 【北京分部】:北京中山學院/福鑫大樓 【南京分部】:金港大廈(和燕路) 【武漢分部】:佳源大廈(高新二路) 【成都分部】:領館區1號(中和大道) 【沈陽分部】:沈陽理工大學/六宅臻品 【鄭州分部】:鄭州大學/錦華大廈 【石家莊分部】:河北科技大學/瑞景大廈 【廣州分部】:廣糧大廈 【西安分部】:協同大廈
            最近開課時間(周末班/連續班/晚班):2019年1月26日
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                    1、培訓過程中,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、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
                    3、培訓合格學員可享受免費推薦就業機會。

            課程大綱
             

            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

             
              備案號:備案號:滬ICP備08026168號-1 .(2024年07月24日)...............
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