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