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    deep learning

    Course Overview

    Deep Learning

    Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

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    Course Content

    Part 1 – Artificial Neural Networks

    All below sections will be implemented with tensorflow and keras, Programming knowledge of tensorflow and keras will be given during model buildings.

    Section 1

    • Artificial Neural networks
    • 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


    Section 2

    • Building an ANN
    • Prerequisites
    • How to get the dataset
    • Business Problem Description
    • Building an ANN – Step 1
    • Building an ANN – Step 2
    • Building an ANN – Step 3
    • Building an ANN – Step 4
    • Building an ANN – Step 5
    • Building an ANN – Step 6
    • Building an ANN – Step 7
    • Building an ANN – Step 8
    • Building an ANN – Step 9
    • Building an ANN – Step 10

    Section 3

    • Homework Challenge – Should we say goodbye to that customer?
    • Homework Instruction
    • Homework Solution

    Section 4

    • Evaluating, Improving and Tuning the ANN
    • Evaluating the ANN
    • Improving the ANN
    • Tuning the ANN

    Part 2 – Convolutional Neural Networks

    Section 5

    • CNN Intuition
    • What you’ll Need for CNN
    • Plan of attack
      What are convolutional neural networks?
    • Step 1 – Convolution Operation
    • Step 1(b) – ReLU Layer
    • Step 2 – Pooling
    • Step 3 – Flattening
    • Step 4 – Full Connection
    • Summary
    • Softmax
    • Cross-Entropy

    Section 6

    • Building a CNN
    • How to get the dataset
    • Introduction to CNNs
    • Building a CNN – Step 1
    • Building a CNN – Step 2
    • Building a CNN – Step 3
    • Building a CNN – Step 4
    • Building a CNN – Step 5
    • Building a CNN – Step 6
    • Building a CNN – Step 7
    • Building a CNN – Step 8
    • Building a CNN – Step 9
    • Building a CNN – Step 10

    Section 7

    • Homework – What’s that pet?
    • Homework Instruction
    • Homework Solution
    • Evaluating, Improving and Tuning the CNN

    Part 3 – Recurrent Neural Networks

    Section 8

    • RNN (Recurrent Neural networks) Intuition
    • What you’ll need for RNN
    • Plan of attack
    • The idea behind Recurrent Neural Networks
    • The Vanishing Gradient Problem
    • LSTMs
    • Practical intuition
    • LSTM Variations
    • Section 9
    • Building a RNN
    • How to get the dataset
    • Building a RNN – Step 1
    • Building a RNN – Step 2
    • Building a RNN – Step 3
    • Building a RNN – Step 4
    • Building a RNN – Step 5
    • Building a RNN – Step 6
    • Building a RNN – Step 7
    • Building a RNN – Step 8
    • Building a RNN – Step 9
    • Building a RNN – Step 10
    • Building a RNN – Step 11
    • Building a RNN – Step 12
    • Building a RNN – Step 13
    • Building a RNN – Step 14
    • Building a RNN – Step 15

    Section 10

    • Evaluating, Improving and Tuning the RNN
    • Evaluating the RNN
    • Improving the RNN
    • Tuning the RNN

    Part 4 – Self Organizing Maps

    Section 11

    • SOMs [Self-Organizing Maps] Intuition
    • Plan of attack
    • How do Self-Organizing Maps Work?
    • Why revisit K-Means?
    • K-Means Clustering (Refresher)
    • How do Self-Organizing Maps Learn? (Part 1)
    • How do Self-Organizing Maps Learn? (Part 2)
    • Live SOM example
    • Reading an Advanced SOM
    • K-means Clustering (part 2)
    • K-means Clustering (part 3)

    Section 12

    • Building a SOM
    • How to get the dataset
    • Building a SOM – Step 1
    • Building a SOM – Step 2
    • Building a SOM – Step 3
    • Building a SOM – Step 4

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    COURSES FEATURES

    Language          : English
    Lectures            : 02
    Certification     : Yes
    Project              : 01
    Duration           : 30 hrs
    Max-Students : 20

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      Feel free to reach us at Phone: +91 9553700070 or mail us at contact@techeclasses.com

      COURSES FEATURES

      Language          : English
      Lectures            : 03
      Certification     : Yes
      Project              : 01
      Duration           : 45 hrs
      Max-Students : 20
      Campus placements, Freshers, Beginners and Working Professionals. For more details enroll now and get a call from us.
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