Neural Networks Training

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Neural Network is the advanced algorithm of Machine Learning, the training introduces the NN algorithms, and helps to understand its working procedure. The training on artificial neural network notes offered by Multisoft Virtual Academy make an encounter with the techniques, which would be helpful in recognizing the pattern based on the large amount of inputs. Nevertheless, the training helps the candidates to gain knowledge on all the aspects of Neural Networks.

After joining the neural networks course, the candidates would learn:

  • How NN algorithms work?
  • How does the Network Architecture look like?
  • Understand the Perceptron learning procedure
  • How neural networks effective in image segmentation
  • Using the calculus in simpler form
Target audience

IT Professionals wish to learn the implementation of the next-generation machine learning techniques by using the neural network algorithm

Prerequisites

Prior joining the Neural Network Training, the candidates should have an understanding of R, the data science and algorithms and the basic understanding of machine learning

1. An Introduction

  • Machine Learning and Neural Nets

2. The Perceptron learning procedure

  • An overview of the main types of neural network architecture

3. The backpropagation learning procedure

  • Learning the weights of a linear neuron

4. Learning feature vectors for words

  • Learning to predict the next word

5. Object recognition with neural nets       

  • Why object recognition is difficult

6. Optimization: How to make the learning go faster

  • What are the mini-batch gradient descent and adaptive learning rates

7. Recurrent neural networks

  • About recurrent neural networks

8. More recurrent neural networks

9. Ways to make neural networks generalize better

  • Building strategies to make neural networks generalize better 

10. Combining multiple neural networks to improve generalization

  • How to combine multiple neural networks to improve generalization 

11. Hopfield nets and Boltzmann machines

12. Restricted Boltzmann machines (RBMs)

  • About Boltzmann machine learning

13. Stacking RBMs to make Deep Belief Nets

14. Deep neural nets with generative pre-training

15. Modeling hierarchical structure with neural nets

16. Recent applications of deep neural nets

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