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Deep Learning related to Artificial Intelligence

Deep Learning related to Artificial Intelligence - Basic

Duration in Hours (4 Hours session) : 60

Duration in Days: 15

DS05 - Deep Learning in AI

Course Code: DDLB35

40 Hours of Theory

20 Hours of Assignment (Lab)

Deep Learning In AI

  1. Biological Neural Network
  2. Artificial Neural Network
  3. Perceptrons
  4. Layers of a Network
  1. Identity Function
  2. Binary step function or Threshold function
  3. Logistic function or Sigmoid function
  4. ReLU function
  5. Hyperbolic Tangent function
  6. Softmax function
  1. Introduction
  2. ANN with Activation functions
  1. The Neuron
  2. The Activation Function
  3. How do Neural Networks work?
  4. How do Neural Networks learn?
  5. Gradient Descent
  6. Stochastic Gradient Descent
  7. Backpropagation
  1. Business Problem Description
  2. Building an ANN – Step by Step (5 Steps)
  3. Revise Regression
  1. Variables
  2. Constants
  3. Placeholders
  4. Graph / Tensor / Session
  1. Introduction
  1. What are convolutional neural networks?
  2. Step 1 – Convolution Operation
  3. Step 1(b) – ReLU Layer
  4. Step 2 – Pooling
  5. Step 3 – Flattening
  6. Step  – Full Connection
  7. Softmax& Cross-Entropy
  1. Business Problem Description
  2. Building an CNN – Step by Step (5 Steps)
  3. Building a CNN – FINAL DEMO
  1. Introduction
  1. Plan of attack
  2. The idea behind RNN
  3. The Vanishing Gradient Problem
  4. LSTMs
  5. Practical intuition
  6. LSTM Variations
  1. Business Problem Description
  2. Building an RNN – Step by Step (15 Steps)
  3. Building a CNN – FINAL DEMO
  1. High school mathematics level
  2. Basic Python programming knowledge
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