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

Quick recap of model building and validation

Introduction to ANN

Hidden Layers in ANN

Back Propagation in ANN

ANN?model building on Python

TensorFlow Introduction

Building?ANN?models in TensorFlow

Keras Introduction

ANN?hyper-parameters

Regularization in ANN

Activation functions

Learning Rate and Momentum

Optimization Algorithms

Basics of Deep Learning

Pre-requite for the course.?

You need to know basics of python coding

You should have working experience on python packages like Pandas, Sk-learn

You need to have basic knowledge on Regression and Logistic Regression

You must know model validation metrics like accuracy, confusion matrix

You? must know concepts like over-fitting and under-fitting

In simple terms, Our Machine Learning Made Easy course on Python is the pre-requite.

Other Details

Datasets, Code and PPT are available in the resources section within the first lecture video of each session.

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

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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