Deep Learning A-Z™: Hands-On Artificial Neural Networks Training

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The Deep Learning A-Z™: Hands-On Artificial Neural Networks Training aims to impart training to the candidates on the essentials that helps in developing an understanding of intuition behind Artificial Neural Networks. Through this Deep Learning A-Z™: Hands-On Artificial Neural Networks Course, the candidates can pave their way to become a data scientist. This training insight the candidates with the tools in improving the models with effective Parameter Tuning, processing the data and evaluating the performance of the models. Moreover, this training insight all on Robust Structure, Intuition Tutorials, Exciting Projects, Hands-on Coding and In-Course Support.

After completing this Deep Learning A-Z™: Hands-On Artificial Neural Networks Training, the candidates would be able to:

  • Understand how to evaluate the performance of the models with the most relevant technique, k-Fold Cross Validation
  • Learn to pre-process the data, in order to learn the models in the most favorable condition
  • Learn how to Self-organize the maps in order to investigate the Fraud
  • Learn about the intuition behind Recurrent Neural Networks
  • Learn how to apply the Recurrent Neural Networks in practice
  • Learn about the intuition behind AutoEncoders
  • Learn how to apply the Artificial Neural Networks in practice
  • Learn how to apply the Boltzmann Machines in practice
  • Develop understanding of the intuition behind Self-Organizing Maps
Target audience
  • Individuals interested in Deep Learning and have at least high school knowledge in math
  • Any intermediate level people who has the knowledge of  the basics of Deep learning, which includes the classical algorithms like linear regression or logistic regression, but who want to learn more about the topics: Artificial Neural Networks and explore all the different fields of Deep Learning.
  • The individuals not very much compatible with the coding, but who are interested in Machine Learning or Deep Learning and want to apply it easily on data sets.
  • The college students, who wish to start a career in Data Science
  • The data analysts, who wish to level up in Deep Learning
  • The individuals, who are planning to switch their jobs to become a data scientist
  • The individuals, who wish to create added value to their business by using powerful Deep Learning tools
Prerequisites

The candidates required to have good knowledge of high school level mathematics

1. Artificial Neural Networks

  • ANN Intuition
    • Plan of Attack
    • The Neuron
    • The Activation Function
    • How do Neural Networks work?
    • How do Neural Networks learn?
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
  • Building an ANN
    • How to get the dataset
    • Business Problem Description
    • Building an ANN
  • Evaluating, Improving and Tuning the ANN
    • Evaluating the ANN
    • Improving the ANN
    • Tuning the ANN

2. Convolutional Neural Networks

  • CNN Intuition
    • Plan of attack
    • What are convolutional neural networks?
    • Convolution Operation
    • ReLU Layer
    • Pooling
    • Flattening
    • Full Connection
    • Softmax & Cross-Entropy
  • Building a CNN
    • How to get the dataset
    • Introduction to CNNs
    • Building a CNN

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