1.1) What means Learning for Artificial Intelligence?
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1.2) How Deep Learning differs from Machine Learning?
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2.1) What is a Tensor? Data Representation for Deep Learning
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2.2) Dot Product to Find the Angle between Vectors
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2.3) Gradient Descent and Directional Derivatives
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2.4) Deterministic vs Stochastic Gradient Descent
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2.5) Stochastic Gradient Descent with Momentum
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2.6) Root Mean Square Propagation (RMSprop)
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3.1) What is Entropy for Information Theory?
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3.2) Why Minimize the Cross-Entropy instead of the Classification Error?
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3.3) How Logistic Sigmoid is related to Softplus Function?
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3.4) Why Softmax as Activation Function?
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4.1) How Feedforward differs from Recurrent Neural Network?
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4.2) Why Rectified Linear Unit (ReLU) for Feedforward Network?
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5.1) Great Jupyter Notebooks for Learning Keras!
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5.2) How to Activate the Free GPU/TPU on Google Colab?
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5.3) Neural Network: Handwritten Digits Classification
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5.4) Sentiment Analysis on Movie Reviews
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5.5) Predicting House Prices with Keras
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