50 Days of Deep Learning
51 videos • 80,743 views • by Mısra Turp
1
A Summary of Deep Learning and Artificial Intelligence Landscape
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2
How Forward Propagation in Neural Networks works
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3
Backward Propagation in Neural Networks explained
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4
Setting up the Prerequisites to Build your First Neural Network
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5
Importing and Preparing a Dataset for Neural Networks
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6
How to Build Your First Neural Network in Python and Keras
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7
How to Evaluate Neural Network Performance
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8
All Hyperparameters of a Neural Network Explained
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9
How Many Hidden Layers and Neurons does a Neural Network Need
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10
Which Loss Function, Optimizer and LR to Choose for Neural Networks
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11
How to Select the Right Activation Function and Batch Size
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12
Weight Initialization and Regularization Techniques for NNs
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13
How to set up the Hyperparameters of a Neural Network
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14
Why Regularization Lowers Overfitting
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15
When Should You Use L1/L2 Regularization
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16
What is Dropout Regularization | How is it different?
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17
Regularization with Data Augmentation and Early Stopping
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18
How to Implement Regularization on Neural Networks
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19
What is Vanishing/Exploding Gradients Problem in NNs
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20
How to Choose the Correct Initializer for your Neural Network
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21
How to Choose an Activation Function for Neural Networks
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22
How Does Batch Normalization Work
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23
Gradient Clipping and How it Helps with Exploding Gradients in Neural Networks
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24
How to Solve Vanishing Gradients in Keras and Python
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25
How to Lower Neural Network Training Times
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26
Normalizing data for better Neural Network performance
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27
How (and Why) to Use Mini-Batches in Neural Networks
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28
How to select the correct optimizer for Neural Networks
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29
Pruning a neural Network for faster training times
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30
How to Use Learning Rate Scheduling for Neural Network Training
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31
How to Make Neural Networks Train Faster on Keras
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32
Part 1: Getting Ready to Build your First Advanced Neural Network
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33
Part 2: Best Settings to Initialize Your NN with
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34
Part 3: Training the Neural Network
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35
Part 4: Tuning the Neural Network for Better Performance
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36
Part 5: Tips and Tricks on How to Initialize Your Neural Network
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37
How to Evaluate a Neural Network's Performance
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38
How to Decide Whether Your Neural Network is Doing Well
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39
What is the Optimal Performance of a Neural Network?
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40
Tuning a Neural Network | Deciding on next steps to take
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41
Simple Methods for Hyperparameter Tuning
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42
Advanced Methods for Hyperparameter Tuning
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43
How to Understand What's Wrong with a Neural Network
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44
How to Tune a Neural Network
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45
Training a Network for Better Performance
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46
Basics of Convolutional Neural Networks
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47
CNN follow along calculations
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48
How to Implement CNNs in Keras
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49
Basics of Recurrent Neural Networks
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50
LSTMs and GRUs
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51
How to Implement RNNs in Keras
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