NEURAL NETWORKS AND DEEP LEARNING
51 videos • 28,029 views • by JOSHUA TALKS
1
1 COURSE OBJECTIVES AND SYLLABUS
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2
WHAT IS Artificial Intelligence
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3
What Is Machine Learning
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4
WHAT IS Deep Learning,When to use ML and DL
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5
MOTIVATION TO NEURAL NETWORKS
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6
The HUMAN BRAIN
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7
MODEL OF A NEURON
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8
WHAT IS BIAS
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9
WHAT IS Artificial Neural Network
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10
ACTIVATION FUNCTION(BINARY, LINEAR, NON -LINEAR)
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11
SIGMOID FUNCTION
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12
TANH FUNCTION
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13
RELU FUNCTION
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14
WHICH ACTIVATION FUNCTION TO USE?
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15
LEAKY, SOFTMAX ACTIVATION FUNCTIONS
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16
INTRODUCTION TO MCCULLOCH-PITTS NEURONAL MODEL
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17
DESIGNING MCCULLOCH PITTS NEURON MODEL FOR LOGIC GATES
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18
NEURAL NETWORK ARCHITECTURES
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19
LEARNING : ERROR BASED LEARNING
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20
MEMORY BASED LEARNING
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21
HEBBIAN LEARNING
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22
COMPETITIVE LEARNING
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23
BOLTZMANN LEARNING
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24
PERCEPTRON AND BASIC TERMINOLOGY
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25
ADAPTIVE FILTERING PROBLEM
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26
LOSS AND COST FUNCTION
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27
OPTIMIZATION TECHNIQUES-PART 1
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28
UNCONSTRAINED OPTIMIZATION TECHNIQUES
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29
LMS ALGORITHM
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30
WHAT IS PERCEPTRON
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31
PERCEPTRON CONVERGENCE THEOREM
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32
MULTI LAYER PERCEPTRON
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33
BACKPROPAGATION ALGORITHM
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34
NUMERICAL EXAMPLE-BACK PROPAGATION-PART-1
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35
NUMERICAL EXAMPLE BACK PROPAGATION-PART 2
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36
THE XOR PROBLEM
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37
CNN-CONVOLUTIONAL NEURAL NETWORKS-PART-1
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38
CONVOLUTIONAL NEURAL NETWORK-PART 2
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39
RNN- RECURRENT NEURAL NETWORKS
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40
RNN PART 2- BACK PROPAGATION IN RNN
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41
BIDIRECTIONAL RNN- LSTM(LONG SHORT TERM MEMORY)
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42
DEEP RNNs
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43
AUTOENCODERS
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44
HOPFIELD NETWORKS PART 1
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45
HOPFIELD NETWORKS PART 2
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46
ENCODER DECODER SEQUENCE TO SEQUENCE ARCHITECTURE
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47
RESTRICTED BOLTZMANN MACHINES
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48
DEEP BELIEF NETWORKS
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49
DEEP BOLTZMANN MACHINES
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50
GENERATIVE ADVERSARIAL NETWORKS(GANS) - PART - 1
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51
GENERATIVE ADVERSARIAL NETWORKS(GANS) - PART - 2
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