Artificial Neural Networks(ANN) Made Easy
Artificial Neural Networks(ANN) Made Easy, available at $29.99, has an average rating of 3.7, with 66 lectures, based on 77 reviews, and has 6976 subscribers.
You will learn about ANN Introduction ANN Model Building ANN Hyper parameters Fine-tuning and Selecting ANN models Shallow and Deep Neural Networks Building ANN Models in Python, TensorFlow and Keras This course is ideal for individuals who are Beginners in Machine Learning or Beginners in TensorFlow or Beginners in Deep Learning or Data Science Aspirants or Computer Vision students or Engineering , Mathematics and science students or Data Analysts and Predictive Modelers It is particularly useful for Beginners in Machine Learning or Beginners in TensorFlow or Beginners in Deep Learning or Data Science Aspirants or Computer Vision students or Engineering , Mathematics and science students or Data Analysts and Predictive Modelers.
Enroll now: Artificial Neural Networks(ANN) Made Easy
Summary
Title: Artificial Neural Networks(ANN) Made Easy
Price: $29.99
Average Rating: 3.7
Number of Lectures: 66
Number of Published Lectures: 66
Number of Curriculum Items: 66
Number of Published Curriculum Objects: 66
Original Price: $59.99
Quality Status: approved
Status: Live
What You Will Learn
- ANN Introduction
- ANN Model Building
- ANN Hyper parameters
- Fine-tuning and Selecting ANN models
- Shallow and Deep Neural Networks
- Building ANN Models in Python, TensorFlow and Keras
Who Should Attend
- Beginners in Machine Learning
- Beginners in TensorFlow
- Beginners in Deep Learning
- Data Science Aspirants
- Computer Vision students
- Engineering , Mathematics and science students
- Data Analysts and Predictive Modelers
Target Audiences
- Beginners in Machine Learning
- Beginners in TensorFlow
- Beginners in Deep Learning
- Data Science Aspirants
- Computer Vision students
- Engineering , Mathematics and science students
- Data Analysts and Predictive Modelers
Course Covers below topics in detail
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Quick recap of model building and validation
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Introduction to ANN
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Hidden Layers in ANN
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Back Propagation in ANN
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ANN model building on Python
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TensorFlow Introduction
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Building ANN models in TensorFlow
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Keras Introduction
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ANN hyper-parameters
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Regularization in ANN
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Activation functions
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Learning Rate and Momentum
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Optimization Algorithms
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Basics of Deep Learning
Pre-requite for the course.
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You need to know basics of python coding
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You should have working experience on python packages like Pandas, Sk-learn
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You need to have basic knowledge on Regression and Logistic Regression
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You must know model validation metrics like accuracy, confusion matrix
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You must know concepts like over-fitting and under-fitting
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In simple terms, Our Machine Learning Made Easy course on Python is the pre-requite.
Other Details
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Datasets, Code and PPT are available in the resources section within the first lecture video of each session.
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Code has been written and tested with latest and stable version of python and tensor-flow as of Sep2018
Course Curriculum
Chapter 1: Pre-requite Machine Learning Basics(Recap Session – Optional)
Lecture 1: Introduction
Lecture 2: Regression
Lecture 3: Regression LAB
Lecture 4: Logistic regression
Lecture 5: logit function
Lecture 6: Building a logistic Regression Line
Lecture 7: Multiple logistic regression.
Lecture 8: Validation Matrices – Classification Matrix
Lecture 9: Sensitivity and Specificity part1
Lecture 10: Sensitivity vs Specificity part2
Lecture 11: Sensitivity Specificity LAB
Lecture 12: ROC and AUC
Lecture 13: ROC and AUC LAB
Lecture 14: The training error
Lecture 15: Over Fitting and Under Fitting
Lecture 16: Bias Variance Trade-off
Lecture 17: Holdout data validation
Lecture 18: Hold Out data validation LAB
Chapter 2: ANN Introduction
Lecture 1: Introduction to ANN
Lecture 2: Logistic Regression Recap
Lecture 3: Decision Boundary – Logistic Regression
Lecture 4: Decision Boundry – LAB
Lecture 5: New Representation for Logistic Regression
Lecture 6: Non Linear Decision Boundary – Problem
Lecture 7: Non Linear Decision Boundary – Solution
Lecture 8: Intermediate Output LAB
Lecture 9: Neural Network Intuition
Lecture 10: Neural Network Algorithm
Lecture 11: Demo Neural Network Algorithm
Lecture 12: Neural Network LAB
Lecture 13: Local Minima and Number of Hidden Layers
Lecture 14: Digit Recognizer Lab
Lecture 15: Conclusion
Chapter 3: Introduction to TensorFlow and Keras
Lecture 1: Introduction to Deep Learning Frameworks
Lecture 2: Key Terms of Tensorflow
Lecture 3: Coding basics in Tensorflow
Lecture 4: Model building intution
Lecture 5: LAB Building Linear and Logistic regression models with Tensorflow
Lecture 6: LAB MNIST model using tensorflow
Lecture 7: Tensorflow shortcomings and Intro to Keras
Lecture 8: LAB MNIST model using Keras
Lecture 9: Tensorflow vs Keras and conclusion
Chapter 4: ANN Hyper-parameters
Lecture 1: Introduction to Hyper-parameters
Lecture 2: LAB_calculating number of parameters
Lecture 3: Regularization
Lecture 4: LAB_Overfitting of a Regression Model
Lecture 5: LAB_Regularization in Regression
Lecture 6: Regularization in Neural Networks
Lecture 7: Demo_Regularization in Neural Networks
Lecture 8: Dropout Regularization
Lecture 9: LAB_ Dropout Regularization.
Lecture 10: Weight sharing in Dropout.
Lecture 11: Early stopping
Lecture 12: LAB_ Early stopping
Lecture 13: Activation Function
Lecture 14: Demo_Activation Function
Lecture 15: Problem of Vanishing Gradients
Lecture 16: ReLU activation Function
Lecture 17: Activation Function for Last Layer
Lecture 18: Learning Rate
Lecture 19: Demo_ Learning Rate
Lecture 20: Momentum
Lecture 21: LAB_ Learning rate and momentum
Lecture 22: Gradient Descent Batches
Lecture 23: LAB_Gradient Descent vs Mini Batch
Lecture 24: Hyper Parameter conclusion
Instructors
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Venkata Reddy AI Classes
Data Science starts here!
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 8 votes
- 3 stars: 16 votes
- 4 stars: 30 votes
- 5 stars: 19 votes
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