Course Content
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Introduction to Deep Learning 0/10
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What is Deep Learning?Lecture1.1
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Advantage of Deep Learning over Machine LearningLecture1.2
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Real-life use cases of Deep LearningLecture1.3
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How Deep Learning works?Lecture1.4
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Feature ExtractionLecture1.5
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Activation FunctionsLecture1.6
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Non-Linear activation functionsLecture1.7
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Derivatives of activation functionLecture1.8
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Variants of Gradient DescentLecture1.9
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Illustrate PerceptronLecture1.10
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Understanding Fundamentals if Neural Network using TensorFlow 0/6
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What is TensorFlow?Lecture2.1
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TensorFlow code-basicsLecture2.2
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Graph VisualizationLecture2.3
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Constants, Placeholders, VariablesLecture2.4
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Creating a ModelLecture2.5
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Step by Step – Use- Case Implementation for regression and classificationLecture2.6
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Deep Dive into Neural Networks using TensorFlow and Keras 0/13
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Understand limitations of a Single PerceptronLecture3.1
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Understand Neural Networks in DetailLecture3.2
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Illustrate Multi- Layer PerceptronLecture3.3
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Supervised Learning with Neural networksLecture3.4
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Building blocks of a deep NNLecture3.5
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Forward PropagationLecture3.6
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Backpropagation- Learning AlgorithmLecture3.7
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Understand Backpropagation- Using Neural Network ExampleLecture3.8
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Digit ClassificationLecture3.9
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Neural Network representationLecture3.10
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Vectorizing across multiple examplesLecture3.11
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Explanation across vectorized implementationLecture3.12
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TensorBoardLecture3.13
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