課程代碼

Nue_LBG

課程時長

14 時間：同常來說包括休息是 2天

最低要求

Knowledge/appreciation of machine learning, systems architecutre and programming languages are desirable

概觀

這個基於課堂的培訓課程將包含演示和基於計算機的示例和案例研究練習，以與相關的神經和深度網絡庫進行

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課程簡介

Overview of neural networks and deep learning
- The concept of Machine Learning (ML)
- Why we need neural networks and deep learning?
- Selecting networks to different problems and data types
- Learning and validating neural networks
- Comparing logistic regression to neural network
Neural network
- Biological inspirations to Neural network
- Neural Networks– Neuron, Perceptron and MLP(Multilayer Perceptron model)
- Learning MLP – backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions appropriate to forecasting and classification
- Parameters – learning rate, regularization, momentum
- Building Neural Networks in Python
- Evaluating performance of neural networks in Python
Basics of Deep Networks
- What is deep learning?
- Architecture of Deep Networks– Parameters, Layers, Activation Functions, Loss functions, Solvers
- Restricted Boltzman Machines (RBMs)
- Autoencoders
Deep Networks Architectures
- Deep Belief Networks(DBN) – architecture, application
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Network
- Recursive Neural Network
- Recurrent Neural Network
Overview of libraries and interfaces available in Python
- Caffee
- Theano
- Tensorflow
- Keras
- Mxnet
- Choosing appropriate library to problem
Building deep networks in Python
- Choosing appropriate architecture to given problem
- Hybrid deep networks
- Learning network – appropriate library, architecture definition
- Tuning network – initialization, activation functions, loss functions, optimization method
- Avoiding overfitting – detecting overfitting problems in deep networks, regularization
- Evaluating deep networks
Case studies in Python
- Image recognition – CNN
- Detecting anomalies with Autoencoders
- Forecasting time series with RNN
- Dimensionality reduction with Autoencoder
- Classification with RBM

客戶評論

★★★★★

它非常互動，比預期更輕鬆和非正式。我們在當時涵蓋了很多主題，培訓師總是樂於接受更詳細的討論，或者更廣泛地討論主題及其相關方式。我覺得培訓給了我繼續學習的工具，相反，它是一次性會議，一旦你完成學習就會停止，這對於主題的規模和復雜性非常重要。

Jonathan Blease

課程:Artificial Neural Networks, Machine Learning, Deep Thinking

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安創造了一個提問和學習的好環境。我們有很多樂趣，同時也學到了很多東西。

Gudrun Bickelq

課程:Introduction to the use of neural networks

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交互式部分，根據我們的特定需求量身定制。

Thomas Stocker

課程:Introduction to the use of neural networks

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我真的很感激克里斯對我們問題的明確答案。

Léo Dubus

課程:Neural Networks Fundamentals using TensorFlow as Example

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我一般都很喜歡知識淵博的教練。

Sridhar Voorakkara

課程:Neural Networks Fundamentals using TensorFlow as Example

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我對這門課程的標準感到驚訝 - 我會說它是大學標準。

David Relihan

課程:Neural Networks Fundamentals using TensorFlow as Example

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非常好的全面概述。 Go OD背景到原因Tensorflow工作，因為它確實。

Kieran Conboy

課程:Neural Networks Fundamentals using TensorFlow as Example

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我喜歡有機會提出問題並對理論進行更深入的解釋。

Sharon Ruane

課程:Neural Networks Fundamentals using TensorFlow as Example

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培訓師很容易解釋困難和高級話題。

Leszek K

課程:Artificial Intelligence Overview

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我喜歡深度機器學習的新見解。

Josip Arneric

課程:Neural Network in R

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我們獲得了一些關於NN的知識，對我來說最有趣的是現在流行的新型NN。

Tea Poklepovic

課程:Neural Network in R

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我最喜歡R :)））中的圖表。

Faculty of Economics and Business Zagreb

課程:Neural Network in R

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非常靈活。

Frank Ueltzhöffer

課程:Artificial Neural Networks, Machine Learning and Deep Thinking

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我一般都很喜歡靈活性。

Werner Philipp

課程:Artificial Neural Networks, Machine Learning and Deep Thinking

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鑑於技術前景：未來哪種技術/流程可能變得更加重要;看，這項技術可以用於什麼。

Commerzbank AG

課程:Neural Networks Fundamentals using TensorFlow as Example

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我從主題選擇中受益。訓練風格。練習方向。

Commerzbank AG

課程:Neural Networks Fundamentals using TensorFlow as Example

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與講師的交流環節

文欣张

課程:Artificial Neural Networks, Machine Learning, Deep Thinking

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都喜歡

lisa xie

課程:Artificial Neural Networks, Machine Learning, Deep Thinking

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很多練習，我可以直接在我的工作中使用。

Alior Bank S.A.

課程:Neural Network in R

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真實數據的例子。

Alior Bank S.A.

課程:Neural Network in R

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神經網絡，循環中的pROC。

Alior Bank S.A.

課程:Neural Network in R

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涵蓋廣泛的主題和領導者的實質性知識。

ING Bank Śląski S.A.; Kamil Kurek Programowanie