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DEEP LEARNING NEURAL NETS WITH MATH DERIVATIONS PART 1

Data Science and Neural Networks Professional Course

All Job Seekers

Online and Classroom Sessions

Week Days and Week Ends

60 Days

•Work with standard programming skills in Data Science and Neural Networks.

•Learn Data Science and Neural Networks best practices and become a blackbelt.

•How to write Data Science and Neural Networks scripts to automate redundant tasks.

•From A-Z: The Complete Beginners-Advanced Masterclass – Learn Data Science and Neural Networks

•Learn how to model in Data Science and Neural Networks with no previous experience

•Learn and Understand Data Science and Neural Networks From a total Beginner to and Expert

•Understand Data Science and Neural Networks and how to use it to write styles programmatically in Data Science and Neural Networks.

•Learn how to code in Data Science and Neural Networks. This class is set up for complete beginners!

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•We Groom up your documents and profiles

•Doubt clarification in class and after class

•Classes are Accessible on Website and Mobile Apps

•Assignments and test to ensure concept absorption.

•Training by Proficient Trainers with more than a decade of experience

•Our trainers have experience in training End Users & Students & Corporate employees.

•We help the students in building the resume boost their knowledge by providing useful Interview tips

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•Devops, Javascript, Aws, Amazon Ec2, Angularjs, Vuejs, React.js, Node.js, Ansible, Docker, Startup, Architectural Design, Machine Learning, Python, Cloud

•Java/J2EE, .Net C#, Networking, Oracle DBA, Embedded Developers, HTML5, Android Framework, Android Developers, MSTR Developer, Cognos, SAN, Windows Admin

•PHP, OpenCart Developer, Magento Developer, Html, Javascript, Jquery, Css, Photoshop, html, css, bootstrap, javascript, jquery, Business Development

•Software Development, Senior Software Developer, Mean Stack, React.js, Mern Stack, Full Stack, Sql, Spark, Scala, Python, Ui Development

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•Promo Video

•The Linear Perceptron

•Introduction To The Classification Problem

•A Simple Glimpse Of Overfitting

•The Perceptron Equation

•Visualization Of The Perceptron Equation

•Proof : Weight Vector Is Perpendicular To The Decision Boundary

•More Visualization For The Perceptron Weights – I

•More Visualization Of The Perceptron Weights – II

•Activation Functions

•Graphical Representation Of A Neural Network

•Types Of Machine Learning

•Solved Example (I) : Single Layer Perceptron Designed Graphically

•Non-Linearly Separable Data And The Multi Layer Perceptron (MLP)

•Introduction To Multi-Layer Perceptrons

•Solved Example (II) : MLP Design Graphically

•Intuition Of Multi-Layer Perceptrons – Part 1

•Intuition Of Multi-Layer Perceptrons – Part 2

•The XOR Problem – Part 1

•The XOR Problem – Part 2

•MultiClass Classification And The Sigmoid Activation

•Vectorized Notation And The Weight Matrix

•Perceptron Learning !

•The Perceptron Learning Rule – Part 1

•The Perceptron Learning Rule – Part 2

•Proof : Perceptron Convergence Theorem – Part 1

•Proof : Perceptron Convergence Theorem – Part 2

•Proof : Perceptron Convergence Theorem – Part 3

•Three Main Problems Of The Threshold Perceptron

•The Gradient Descent Algorithm

•The Error Function

•The Sigmoid Activation Function Again

•Deriving The Gradient Descent Algorithm

•Notes About Gradient Descent

•More Notes And filling Up

•Solved Example (III) : Gradient Descent Convergence

•Solved Example (IIII) : MLP With Linear Activations

•The Back-Propagation Algorithm !

•Derivation Of Back Propagation – Part 1

•Derivation Of Back Propagation – Part 2

•Derivation Of Back Propagation – Part 3

•Vectorization Of BackPropagation – Part 1

•Vectorization Of BackPropagation – Part 2

•Vectorization Of BackPropagation – Part 3

•Vectorization Of BackPropagation – Part 4

•Vectorization Of BackPropagation – Part 5 – Batch Vectorization

•Regularization !

•Regression, Overfitting, And Underfitting

•Introduction To Reglarization

•Different Ways For Regularization

•L1 vs L2 Regularization – Part 1 – Gradient Descent

•L1 vs L2 Regularization -Part 2 – Numerical, Intuitive, And Graphical Comparison

•Dropout ! – Intuition

•Dropout vs Inverted Dropout

•Dropout in a nutshell

•Cross-Validation : How Do I Know I Am Overfitting Or Underfitting ?

•Model Performance Metrics !

•Class Imbalance – Why Is Accuracy Not Always The Best Metric ?

•Precision – Recall , And F1 Score

•F1 Score vs Simple Average

•Precision-Recall Curve

•ROC and AUC

•Improving Neural Network Performance – Part (I)

•Gradient Descent With Momentum – Part 1

•Gradient Descent With Momentum – Part 2

•Adagrad And RMSProb

•Adam And Learning Rate Decay

•The Vanishing Gradient Problem

•Input Centering And Normalization – Part 1

•Input Centering And Normalization – Part 2

•Weight Initialization – Part 1 – The Symmetry Problem

•Weight Initialization – Part 2

•Changing Activation Functions – Tanh – Relu – LeakyRelu

•Maximum Likelihood Estimation Review

•Maximum Likelihood Estimation – Quick Overview

•Maximum Likelihood Estimation Of Gaussian Distribution Parameters

•Improving Neural Network Performance – Part (II)

•The Sigmoid And Bernoulli Distribution

•The Cross Entropy Cost Function – Derivation

•The Cross Entropy & The Vanishing Gradient Problem

•Cross Entropy In Multi-Class Problems

•The Softmax Activation Function

•BackPropagation Derivation For The Softmax Activation Function

•Notes About Softmax

•Batch Normalization !

•Introduction To Batch Normalization – Part 1

•Introduction To Batch Normalization – Part 2

•Forward Pass Equations For Batch Normalization

•Batch Normalization :: Inference

•Derivation Of Back Propagation Through Batch Normalization – Part (I)

•Derivation Of Back Propagation Though Batch Normalization – Part 2

•Bonus Section !

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