Deep Learning A-Z Hands-On Artificial Neural Networks
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       Video Length : 30h40m0s
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       Tasks Number : 206
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       Students Enrolled : 948
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       Medium Level
  • Curriculum
  • 1. Welcome to the course
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      What is Deep Learning?
      10m0s
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      Installing Python
      10m0s
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      How to get the dataset
      10m0s
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      Some Additional Resources!!
      10m0s
  • 2. Part 1 - Artificial Neural Networks
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      ANN Intuition
      10m0s
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      Plan of Attack
      10m0s
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      The Neuron
      10m0s
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      The Activation Function
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      How do Neural Networks work?
      10m0s
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      How do Neural Networks learn?
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      Gradient Descent
      10m0s
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      Stochastic Gradient Descent
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      Back propagation
      10m0s
  • 3. Building an ANN
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      Prerequisites
      10m0s
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      How to get the dataset
      10m0s
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      Business Problem Description
      10m0s
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      Installing Keras
      10m0s
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      Building an ANN - Step 1
      10m0s
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      Building an ANN - Step 2
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      Building an ANN - Step 3
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      Building an ANN - Step 4
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      Building an ANN - Step 5
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      Building an ANN - Step 6
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      Building an ANN - Step 7
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      Building an ANN - Step 8
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      Building an ANN - Step 9
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      Building an ANN - Step 10
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  • 4. Homework Challenge - Should we say goodbye to that customer ?
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      Homework Instruction
      10m0s
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      Homework Solution
      10m0s
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      Evaluating, Improving and Tuning the ANN
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      Evaluating the ANN
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      Improving the ANN
      10m0s
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      Tuning the ANN
      10m0s
  • 5. Homework Challenge - Put me one step down on the podium
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      Homework Instruction
      10m0s
  • 6. Part 2 - Convolutional Neural Networks
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      CNN Intuition
      10m0s
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      What You'll Need for CNN
      10m0s
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      Plan of attack
      10m0s
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      What are convolutional neural networks?
      10m0s
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      Step 1 - Convolution Operation
      10m0s
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      Step 1(b) - ReLU Layer
      10m0s
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      Step 2 - Pooling
      10m0s
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      Step 3 - Flattening
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      Step 4 - Full Connection
      10m0s
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      Summary
      10m0s
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      Softmax & Cross-Entropy
      10m0s
  • 7. Building a CNN
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      How to get the dataset
      10m0s
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      Installing Keras
      10m0s
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      Introduction to CNNs
      10m0s
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      Building a CNN - Step 1
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      Building a CNN - Step 2
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      Building a CNN - Step 3
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      Building a CNN - Step 4
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      Building a CNN - Step 5
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      Building a CNN - Step 6
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      Building a CNN - Step 7
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      Building a CNN - Step 8
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      Building a CNN - Step 9
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      Building a CNN - Step 10
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  • 8. Homework - What's that pet ?
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      Homework Instruction
      10m0s
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      Homework Solution
      10m0s
  • 9. Evaluating, Improving and Tuning the CNN
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      Homework Challenge - Get the gold medal
      10m0s
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      Homework Challenge Solution - Get the gold medal
      10m0s
  • 10. Part 3 - Recurrent Neural Networks
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      RNN Intuition
      10m0s
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      What You'll Need for RNN
      10m0s
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      Plan of attack
      10m0s
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      The idea behind Recurrent Neural Networks
      10m0s
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      The Vanishing Gradient Problem
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      LSTMs
      10m0s
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      Practical intuition
      10m0s
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      EXTRA: LSTM Variations
      10m0s
  • 11. Building a RNN
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      How to get the dataset
      10m0s
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      Installing Keras
      10m0s
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      Building a RNN - Step 1
      10m0s
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      Building a RNN - Step 2
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      Building a RNN - Step 3
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      Building a RNN - Step 4
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      Building a RNN - Step 5
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      Building a RNN - Step 6
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      Building a RNN - Step 7
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      Building a RNN - Step 8
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      Building a RNN - Step 9
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      Building a RNN - Step 10
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      Building a RNN - Step 11
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      Building a RNN - Step 12
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      Building a RNN - Step 13
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      Building a RNN - Step 14
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      Building a RNN - Step 15
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  • 12. Evaluating, Improving and Tuning the RNN
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      Evaluating the RNN
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      Improving the RNN
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      Tuning the RNN
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  • 13. Part 4 - Self Organizing Maps
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      SOMs Intuition
      10m0s
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      Plan of attack
      10m0s
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      How do Self-Organizing Maps Work?
      10m0s
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      Why revisit K-Means?
      10m0s
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      K-Means Clustering (Refresher)
      10m0s
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      How do Self-Organizing Maps Learn? (Part 1)
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      How do Self-Organizing Maps Learn? (Part 2)
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      Live SOM example
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      Reading an Advanced SOM
      10m0s
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      EXTRA: K-means Clustering (part 2)
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      EXTRA: K-means Clustering (part 3)
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  • 14. Building a SOM
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      How to get the dataset
      10m0s
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      Building a SOM - Step 1
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      Building a SOM - Step 2
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      Building a SOM - Step 3
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      Building a SOM - Step 4
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  • 15. Mega Case Study
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      Mega Case Study - Step 1
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      Mega Case Study - Step 2
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      Mega Case Study - Step 3
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      Mega Case Study - Step 4
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  • 16. Part 5 - Boltzmann Machines
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      Boltzmann Machine Intuition
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      Plan of attack
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      Boltzmann Machine
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      Energy-Based Models (EBM)
      10m0s
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      Editing Wikipedia - Our Contribution to the World
      10m0s
    • videocam
      Restricted Boltzmann Machine
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      Contrastive Divergence
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      Deep Belief Networks
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      Deep Boltzmann Machines
      10m0s
  • 17. Building a Boltzmann Machine
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      How to get the dataset
      10m0s
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      Installing PyTorch
      10m0s
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      Building a Boltzmann Machine - Introduction
      10m0s
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      Same Data Preprocessing in Parts 5 and 6
      10m0s
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      Building a Boltzmann Machine - Step 1
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      Building a Boltzmann Machine - Step 2
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      Building a Boltzmann Machine - Step 3
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      Building a Boltzmann Machine - Step 4
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      Building a Boltzmann Machine - Step 5
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      Building a Boltzmann Machine - Step 6
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      Building a Boltzmann Machine - Step 7
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      Building a Boltzmann Machine - Step 8
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      Building a Boltzmann Machine - Step 9
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      Building a Boltzmann Machine - Step 10
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      Building a Boltzmann Machine - Step 11
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      Building a Boltzmann Machine - Step 12
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      Building a Boltzmann Machine - Step 13
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      Building a Boltzmann Machine - Step 14
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      Evaluating the Boltzmann Machine
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  • 18. Part 6 - AutoEncoders
    • videocam
      AutoEncoders Intuition
      10m0s
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      Plan of attack
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      Auto Encoders
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      A Note on Biases
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      Training an Auto Encoder
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      Overcomplete hidden layers
      10m0s
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      Sparse Autoencoders
      10m0s
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      Denoising Autoencoders
      10m0s
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      Contractive Autoencoders
      10m0s
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      Stacked Autoencoders
      10m0s
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      Deep Autoencoders
      10m0s
  • 19. Building an AutoEncoder
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      How to get the dataset
      10m0s
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      Installing PyTorch
      10m0s
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      Same Data Preprocessing in Parts 5 and 6
      10m0s
    • videocam
      Building an AutoEncoder - Step 1
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      Building an AutoEncoder - Step 2
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      Building an AutoEncoder - Step 3
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    • videocam
      Homework Challenge - Coding Exercise
      10m0s
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      Building an AutoEncoder - Step 4
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      Building an AutoEncoder - Step 5
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      Building an AutoEncoder - Step 6
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      Building an AutoEncoder - Step 7
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      Building an AutoEncoder - Step 8
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      Building an AutoEncoder - Step 9
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      Building an AutoEncoder - Step 10
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      Building an AutoEncoder - Step 11
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  • 20. Annex - Get the Machine Learning Basics
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      Regression & Classification Intuition
      10m0s
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      What You Need for Regression & Classification
      10m0s
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      Simple Linear Regression Intuition - Step 1
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      Simple Linear Regression Intuition - Step 2
      10m0s
    • videocam
      Multiple Linear Regression Intuition
      10m0s
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      Logistic Regression Intuition
      10m0s
  • 21. Data Preprocessing Template
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      Data Preprocessing - Step 1
      10m0s
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      Data Preprocessing - Step 2
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      Data Preprocessing - Step 3
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      Data Preprocessing - Step 4
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      Data Preprocessing - Step 5
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      Data Preprocessing - Step 6
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    • videocam
      Data Preprocessing Template
      10m0s
  • 22. Classification Template
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      Logistic Regression Implementation - Step 1
      10m0s
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      Logistic Regression Implementation - Step 2
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      Logistic Regression Implementation - Step 3
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      Logistic Regression Implementation - Step 4
      10m0s
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      Logistic Regression Implementation - Step 5
      10m0s
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      Classification Template
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Deep Learning A-Z Hands-On Artificial Neural Networks


Deep Learning A-Z Hands-On Artificial Neural Networks

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