Канал: Datascience Concepts

CNN Important Case Studies - Lecture 11

CNN Important Case Studies - Lecture 11

New Frontiers in AI and ML

New Frontiers in AI and ML

Uncertainty in Deep Learning

Uncertainty in Deep Learning

Introduction to Large Scale ML/AI and Experimentation

Introduction to Large Scale ML/AI and Experimentation

Bias, Interpretability, Fairness in ML and AI - Lecture 22

Bias, Interpretability, Fairness in ML and AI - Lecture 22

Intro to Association Rule Learning, Recommender Systems - Lecture 21

Intro to Association Rule Learning, Recommender Systems - Lecture 21

Introduction to Boltzmann Machines - Lecture 20

Introduction to Boltzmann Machines - Lecture 20

Open AI Gym , Applications in Reinforcement Learning

Open AI Gym , Applications in Reinforcement Learning

Deep Q-Learning (DQN) - Lecture 18

Deep Q-Learning (DQN) - Lecture 18

Bellman Equation and Optimality (Reinforcement Learning) - Lecture 17

Bellman Equation and Optimality (Reinforcement Learning) - Lecture 17

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

Radial basis Function Networks - Lecture 15

Radial basis Function Networks - Lecture 15

Self Organizing Maps (SOMs) - Lecture 14

Self Organizing Maps (SOMs) - Lecture 14

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

Introduction to Autoencoders (SAEs, DAEs ,VAEs)

Introduction to Autoencoders (SAEs, DAEs ,VAEs)

CNN Architectures: GoogLeNet, ResNets, VGG16

CNN Architectures: GoogLeNet, ResNets, VGG16

Introduction to Convolution Neural Networks (CNNs)

Introduction to Convolution Neural Networks (CNNs)

Intro to Attention Mechanisms, Transformers in NLP-Lecture 10

Intro to Attention Mechanisms, Transformers in NLP-Lecture 10

Intro to Natural Language Processing (NLP)-Word2Vec Embeddings-Lecture 8.

Intro to Natural Language Processing (NLP)-Word2Vec Embeddings-Lecture 8.

Introduction to Sequence Models (RNNs)-Lecture 7

Introduction to Sequence Models (RNNs)-Lecture 7

Lecture 6: Machine learning Projects-Important Considerations

Lecture 6: Machine learning Projects-Important Considerations

Lecture 5 - Regularization, Early Stopping, Dropout.

Lecture 5 - Regularization, Early Stopping, Dropout.

Lecture 4: Introduction to Artificial Neural Networks

Lecture 4: Introduction to Artificial Neural Networks

Decision Trees and Ensemble Learning

Decision Trees and Ensemble Learning

Projects in ML/AI-Lecture 2- Gradient Descent (Optimization Algorithms; Adams, RMSProp)

Projects in ML/AI-Lecture 2- Gradient Descent (Optimization Algorithms; Adams, RMSProp)

Projects In Machine Learning and AI - Lecture 1

Projects In Machine Learning and AI - Lecture 1

Instantutor Demo

Instantutor Demo

Nested Loops and Lists

Nested Loops and Lists

List Methods

List Methods

Lists and Strings (Similarities and differences)

Lists and Strings (Similarities and differences)

Creating Lists in Python

Creating Lists in Python

String Methods

String Methods

String Functions and Operations

String Functions and Operations

Strings in Python

Strings in Python

Logical Operators in Python

Logical Operators in Python

If-Elif-Else in Python

If-Elif-Else in Python

Input/ Output and Boolean type in Python

Input/ Output and Boolean type in Python

Syntax and Semantic Errors

Syntax and Semantic Errors

Numbers and Calculation

Numbers and Calculation

Variables in Python

Variables in Python

Getting Started with Repl.it

Getting Started with Repl.it

CS1-Spring2021-RPI-Final exam review

CS1-Spring2021-RPI-Final exam review

CS1-Spring2021-RPI-Lecture 24

CS1-Spring2021-RPI-Lecture 24

CS1-Spring2021-RPI-Lecture 23

CS1-Spring2021-RPI-Lecture 23

CS1-Spring2021-RPI-Lecture 21

CS1-Spring2021-RPI-Lecture 21

CS1-Spring2021-RPI-Lecture 20

CS1-Spring2021-RPI-Lecture 20

Lecture 27 - Part 2 - Principal Component Analysis Explained

Lecture 27 - Part 2 - Principal Component Analysis Explained

Lecture 27 - Part 1 - Input Preprocessing

Lecture 27 - Part 1 - Input Preprocessing

CS1-Spring2021-RPI-Lecture 19

CS1-Spring2021-RPI-Lecture 19

Lecture 26 - Part 2 - Kernel Applications

Lecture 26 - Part 2 - Kernel Applications

Lecture 26 - Part 1 - Popular Kernel Machines

Lecture 26 - Part 1 - Popular Kernel Machines

CS1-Spring2021-RPI-Lecture 18

CS1-Spring2021-RPI-Lecture 18

Lecture 25 - Part 3 - Kernel Trick (Infinite Dimensional Feature Space)

Lecture 25 - Part 3 - Kernel Trick (Infinite Dimensional Feature Space)

Lecture 25- Part 2 - Dual Version - Optimal Hyperplane Problem

Lecture 25- Part 2 - Dual Version - Optimal Hyperplane Problem

CS1-Spring2021-RPI-Exam 2 Review

CS1-Spring2021-RPI-Exam 2 Review

Lecture 25 - Part 1 - KKT Conditions for Optimality

Lecture 25 - Part 1 - KKT Conditions for Optimality

Lecture 24 -Part 4 - Soft Margin Optimal Hyperplane (SVMs)

Lecture 24 -Part 4 - Soft Margin Optimal Hyperplane (SVMs)

Lecture 24 - Part 3 - Wider Hyperplanes are better (SVMs)

Lecture 24 - Part 3 - Wider Hyperplanes are better (SVMs)

CS1- Spring 2021-RPI- Lecture 17

CS1- Spring 2021-RPI- Lecture 17

Lecture 24 - Part 2 - Experimental Evidence that Optimal Hyperplane is better (SVMs)

Lecture 24 - Part 2 - Experimental Evidence that Optimal Hyperplane is better (SVMs)

Lecture 24-Part 1- Why is widest hyperplabe Better? (SVMs)

Lecture 24-Part 1- Why is widest hyperplabe Better? (SVMs)

Lecture 23 - Part 3 - Optimal Hyperplane (SVMs)

Lecture 23 - Part 3 - Optimal Hyperplane (SVMs)

Lecture 23 - Part 2- Maximum Margin Hyperplane (SVMs)

Lecture 23 - Part 2- Maximum Margin Hyperplane (SVMs)

CS1-Spring2021-RPI-Lecture 16

CS1-Spring2021-RPI-Lecture 16

Lecture 23 - Part 1 - Support Vector Machines (Maximum Margin Classifier)

Lecture 23 - Part 1 - Support Vector Machines (Maximum Margin Classifier)

Lecture 22 - Part 3 - Improving Gradient Descent in Deep Networks

Lecture 22 - Part 3 - Improving Gradient Descent in Deep Networks

Lecture 22 - Part 2 - Early Stopping and Regularization in Neural Networks

Lecture 22 - Part 2 - Early Stopping and Regularization in Neural Networks

Lecture 22 - Part 1 - Approximation Vs Generalization in Neural (Deep) Networks

Lecture 22 - Part 1 - Approximation Vs Generalization in Neural (Deep) Networks

CS1- Spring 2021-RPI-Lecture 15

CS1- Spring 2021-RPI-Lecture 15

Lecture 21 - Part 3 - Back-Propagation Algorithm

Lecture 21 - Part 3 - Back-Propagation Algorithm

CS1-Spring2021-RPI- Lecture 14

CS1-Spring2021-RPI- Lecture 14

Lecture 21- Part 2 - Fitting Data Efficiently (Neural Networks)

Lecture 21- Part 2 - Fitting Data Efficiently (Neural Networks)

Lecture 21 - Part 1 - Forward Propagation Algorithm

Lecture 21 - Part 1 - Forward Propagation Algorithm

Lecture 20 - Part 3 - The Neural Network

Lecture 20 - Part 3 - The Neural Network

Lecture20 - Part 2 - Universal Approximation

Lecture20 - Part 2 - Universal Approximation

Lecture 20 - Part 1 - Multilayer Perceptron (MLP)

Lecture 20 - Part 1 - Multilayer Perceptron (MLP)

Lecture 19 - Part 2 - K-Means, Gaussian Mixture Models

Lecture 19 - Part 2 - K-Means, Gaussian Mixture Models

Lecture 19 - Part 1 - Unsupervised Learning

Lecture 19 - Part 1 - Unsupervised Learning

CS1-Spring2021-RPI-Lecture 13

CS1-Spring2021-RPI-Lecture 13

CS1-Spring2021-RPI-Lecture 12

CS1-Spring2021-RPI-Lecture 12

Lecture 18 - Part 3 -Radial Basis Functions Network

Lecture 18 - Part 3 -Radial Basis Functions Network

Lecture 18 - Part 2- Parametric Radial Basis Functions

Lecture 18 - Part 2- Parametric Radial Basis Functions

Lecture 18 - Part 1 - Non Parametric RBFs

Lecture 18 - Part 1 - Non Parametric RBFs

CS1-Spring 2021-RPI-Lecture 11

CS1-Spring 2021-RPI-Lecture 11

Lecture 17 - Part 3 - Branch and Bound Nearest Neighbor

Lecture 17 - Part 3 - Branch and Bound Nearest Neighbor

Lecture 17 - Part 2 - Condensed Nearest Neighbor Algorithm

Lecture 17 - Part 2 - Condensed Nearest Neighbor Algorithm

Lecture 17 - Part 1 - Memory and Efficiency of Nearest Neighbor

Lecture 17 - Part 1 - Memory and Efficiency of Nearest Neighbor

CS1-Spring2021-RPI-Lecture 10

CS1-Spring2021-RPI-Lecture 10

Lecture 16 - Part 3 - K Nearest Neighbor Algorithm

Lecture 16 - Part 3 - K Nearest Neighbor Algorithm

Lecture 16 - Part 2 - Nearest Neighbor is 2 Optimal

Lecture 16 - Part 2 - Nearest Neighbor is 2 Optimal

Lecture 16 - Part 1 - Similarity Based Methods

Lecture 16 - Part 1 - Similarity Based Methods

CS1-Spring 2021-RPI-Lecture 9

CS1-Spring 2021-RPI-Lecture 9

Lecture 15 - Conclusion of Foundations in Machine Learning.

Lecture 15 - Conclusion of Foundations in Machine Learning.

Lecture 14 - Part 3 - Data Snooping

Lecture 14 - Part 3 - Data Snooping

Lecture 14-Part 2- Sampling Bias

Lecture 14-Part 2- Sampling Bias

Lecture14 - Part 1 - Three Learning Principles

Lecture14 - Part 1 - Three Learning Principles

CS1-Spring2021-RPI-Exam 1 Review Session

CS1-Spring2021-RPI-Exam 1 Review Session

Lecture 13 - Part 4 - K Fold Cross Validation

Lecture 13 - Part 4 - K Fold Cross Validation

Lecture 13 - Part 3- Model Selection Using Validation

Lecture 13 - Part 3- Model Selection Using Validation

Lecture 13 - Part 2 - What is Validation?

Lecture 13 - Part 2 - What is Validation?

Lecture 13 - Part 1- Validation Intuition

Lecture 13 - Part 1- Validation Intuition

CS1-Spring2021-RPI_Lecture 8

CS1-Spring2021-RPI_Lecture 8

Lecture 12- Part 3 - Weight Decay Augmented Error

Lecture 12- Part 3 - Weight Decay Augmented Error

Lecture 12 - Part 2 -Unconstrained Error

Lecture 12 - Part 2 -Unconstrained Error

CS1-Spring 2021-RPI-Lecture 7

CS1-Spring 2021-RPI-Lecture 7

Lecture 12 - Part 1 - Intuition behind Regularization

Lecture 12 - Part 1 - Intuition behind Regularization

Lecture 11 - Part 3 - Bias Variance with Noise

Lecture 11 - Part 3 - Bias Variance with Noise

Lecture 11 - Part 2 - Measure of Overfit

Lecture 11 - Part 2 - Measure of Overfit

Lecture 11 - Part 1- Intuition of Overfitting

Lecture 11 - Part 1- Intuition of Overfitting

Lecture 10 - Part 2 - Choose Feature Transform

Lecture 10 - Part 2 - Choose Feature Transform

CS1-Spring2021-RPI-Lecture 6

CS1-Spring2021-RPI-Lecture 6

Lecture 10 - Part 1 - Non Linear Feature Transform

Lecture 10 - Part 1 - Non Linear Feature Transform

Lecture 9 - Part 4 - Stochastic Gradient Descent

Lecture 9 - Part 4 - Stochastic Gradient Descent

Lecture 9 - Part 3- Gradient Descent

Lecture 9 - Part 3- Gradient Descent

Lecture 9 - Part 2 - Cross Entropy Error

Lecture 9 - Part 2 - Cross Entropy Error

CS1-Spring2021-RPI-Lecture 5

CS1-Spring2021-RPI-Lecture 5

Lecture 9 - Part 1- Logistic Regression

Lecture 9 - Part 1- Logistic Regression

CS1-Spring2021-RPI-Lecture 4

CS1-Spring2021-RPI-Lecture 4

Lecture 8 - Part 4 - Pseudo Inverse Algorithm

Lecture 8 - Part 4 - Pseudo Inverse Algorithm

Lecture 8 - Part 3 - Linear Regression

Lecture 8 - Part 3 - Linear Regression

Lecture 8- Part 2- PLA on Digits Data

Lecture 8- Part 2- PLA on Digits Data

Lecture 8-Part 1- Linear Model Fundamentals

Lecture 8-Part 1- Linear Model Fundamentals

CS1-Spring 2021-RPI-Lecture 3

CS1-Spring 2021-RPI-Lecture 3

Lecture 7 - Part 7 - Learning Curves

Lecture 7 - Part 7 - Learning Curves

Lecture 7 - Part 6 - Bias-Variance Vs. Out of sample error

Lecture 7 - Part 6 - Bias-Variance Vs. Out of sample error

Lecture 7 - Part 5- Bias Variance Trade-off

Lecture 7 - Part 5- Bias Variance Trade-off

CS1-Spring 2021-RPI-Lecture 2

CS1-Spring 2021-RPI-Lecture 2

Lecture 7 - Part 4 - Sample Complexity

Lecture 7 - Part 4 - Sample Complexity

Lecture 7 - Part 3 - Perceptron's VC dimension

Lecture 7 - Part 3 - Perceptron's VC dimension

CS1-Spring 2021-RPI-Lecture 1

CS1-Spring 2021-RPI-Lecture 1

Lecture 7 - Part 2 - Examples of VC Dimension

Lecture 7 - Part 2 - Examples of VC Dimension

Lecture 7 - Part 1- VC Dimension Definition

Lecture 7 - Part 1- VC Dimension Definition

Lecture 6 - Part 6 - The VC Bound

Lecture 6 - Part 6 - The VC Bound

Lecture 6 - Part 5 - Relationship of growth function and the bound

Lecture 6 - Part 5 - Relationship of growth function and the bound

Lecture 6 - Part 4 - Polynomial bound

Lecture 6 - Part 4 - Polynomial bound

Lecture 6 - Part 3 - Recursive form

Lecture 6 - Part 3 - Recursive form

Lecture 6 - Part 2 - Combinatorial Quantity

Lecture 6 - Part 2 - Combinatorial Quantity

Lecture 6 - Part 1- Growth Function and Break Points

Lecture 6 - Part 1- Growth Function and Break Points

Lecture 5 - Part 5 - Combinatorial Puzzle

Lecture 5 - Part 5 - Combinatorial Puzzle

Lecture 5 - Part 4 - Dichotomy Examples

Lecture 5 - Part 4 - Dichotomy Examples

Lecture 5 - Part 3 - Deficiency in Dichotomies

Lecture 5 - Part 3 - Deficiency in Dichotomies

Lecture 5 - Part 2 - Growth Function

Lecture 5 - Part 2 - Growth Function

Lecture 5 -Part 1- Effective Number of Hypothesis

Lecture 5 -Part 1- Effective Number of Hypothesis

Lecture 4 - Part 5 - Pointwise Error in Learning

Lecture 4 - Part 5 - Pointwise Error in Learning

Lecture 4 - Part 4 - Summarize Feasibility of Learning

Lecture 4 - Part 4 - Summarize Feasibility of Learning

Lecture 4 - Part 3 - Complexity Vs Error Trade-off

Lecture 4 - Part 3 - Complexity Vs Error Trade-off

Lecture 4 - Part 2 - Feasibility of Learning

Lecture 4 - Part 2 - Feasibility of Learning

Lecture 4 - Part 1 - Feasibility of Learning

Lecture 4 - Part 1 - Feasibility of Learning

Lecture 3 - Part 4 - Verification Vs Learning

Lecture 3 - Part 4 - Verification Vs Learning

Lecture 3 - Part 3 - Bin Vs Learning

Lecture 3 - Part 3 - Bin Vs Learning

Lecture 3- Part 2- Hoeffding's Bound

Lecture 3- Part 2- Hoeffding's Bound

Lecture 3 - Part 1-Is learning Feasible?

Lecture 3 - Part 1-Is learning Feasible?

Lecture 2 - Part 3 - Perceptron Learning Algorithm

Lecture 2 - Part 3 - Perceptron Learning Algorithm

Lecture 2 - Part 4 - Other Paradigms of Learning

Lecture 2 - Part 4 - Other Paradigms of Learning

Lecture 2 - Part 2 - The Perceptron

Lecture 2 - Part 2 - The Perceptron

Lecture 2 - Part 1 -The Perceptron

Lecture 2 - Part 1 -The Perceptron

Lecture 1 - Part 3 - Machine Learning from Data

Lecture 1 - Part 3 - Machine Learning from Data

Lecture 1-Part 1- Machine Learning from Data

Lecture 1-Part 1- Machine Learning from Data

Lecture 1- Part 2 -Machine learning from Data

Lecture 1- Part 2 -Machine learning from Data

CS1 Final Exam Review | Live Lecture Series Fall 2020

CS1 Final Exam Review | Live Lecture Series Fall 2020

CS1 Lecture 24| Live Lecture Series Fall 2020

CS1 Lecture 24| Live Lecture Series Fall 2020

CS1 Lecture 22| Live Lecture Series Fall 2020

CS1 Lecture 22| Live Lecture Series Fall 2020

CS1 Lecture 23| Live Lecture Series Fall 2020

CS1 Lecture 23| Live Lecture Series Fall 2020

Review Lecture exam 3

Review Lecture exam 3

CS1 Lecture 21| Live Lecture Series Fall 2020

CS1 Lecture 21| Live Lecture Series Fall 2020

CS1 Lecture 20| Live Lecture Series Fall 2020

CS1 Lecture 20| Live Lecture Series Fall 2020

CS1 Lecture 19| Live Lecture Series Fall 2020

CS1 Lecture 19| Live Lecture Series Fall 2020

CS1 Lecture18| Live Lecture Series Fall 2020

CS1 Lecture18| Live Lecture Series Fall 2020

CS1 Lecture 17| Live Lecture Series 2020

CS1 Lecture 17| Live Lecture Series 2020

CS1 Lecture 16| Live Lecture Series 2020

CS1 Lecture 16| Live Lecture Series 2020

CS1 Lecture 15| Live Lecture Series 2020 Fall

CS1 Lecture 15| Live Lecture Series 2020 Fall

CS1 Lecture 14 | Live Lecture Series 2020

CS1 Lecture 14 | Live Lecture Series 2020

CS1 Lecture 13| Live Lecture Series 2020

CS1 Lecture 13| Live Lecture Series 2020

CS1 Lecture 12 | Live Lecture Series 2020

CS1 Lecture 12 | Live Lecture Series 2020

CS1 Lecture 11 | Live Lecture Series 2020

CS1 Lecture 11 | Live Lecture Series 2020

CS1 Lecture 10| Live Lecture Series

CS1 Lecture 10| Live Lecture Series

Women In Data Science Conference Presentation

Women In Data Science Conference Presentation

Lecture 6 CS1 | Live Lecture Series for fall 2020

Lecture 6 CS1 | Live Lecture Series for fall 2020

Lecture 7 CS1 | Live Lecture Series fall 2020

Lecture 7 CS1 | Live Lecture Series fall 2020

Lecture 8 CS1| Live Lecture Series Fall 2020

Lecture 8 CS1| Live Lecture Series Fall 2020

CS1 Lecture 9| Live Lecture Series

CS1 Lecture 9| Live Lecture Series

Lecture 2 CS1 2020| Live Lecture Series

Lecture 2 CS1 2020| Live Lecture Series

Recursion based Interview Problems

Recursion based Interview Problems

Understand Regularization python sklearn| Machine Learning Tutorial part 21

Understand Regularization python sklearn| Machine Learning Tutorial part 21

Intro Lecture For CS1 | Live Lecture Series for Fall 2020

Intro Lecture For CS1 | Live Lecture Series for Fall 2020

Principal Component Analysis Explained

Principal Component Analysis Explained

Queue and Deque interview problem solutions | Problem solving using Data Structures Part 4D

Queue and Deque interview problem solutions | Problem solving using Data Structures Part 4D

Hierarchical Clustering Python Explained | Machine Learning Tutorial part 19

Hierarchical Clustering Python Explained | Machine Learning Tutorial part 19

Stack based interview problems |Problem Solving using Data Structures and Algorithms part 4C

Stack based interview problems |Problem Solving using Data Structures and Algorithms part 4C

Learn k-means clustering step by step | Machine Learning Tutorial part 18

Learn k-means clustering step by step | Machine Learning Tutorial part 18

Understand queue and deque for interviews| Problem solving using data structures| Part 4B

Understand queue and deque for interviews| Problem solving using data structures| Part 4B

KNN Algorithm python sklearn | machine learning Tutorial Part 17

KNN Algorithm python sklearn | machine learning Tutorial Part 17

Stacks in Problem Solving with Data Structures | Part 4(A)

Stacks in Problem Solving with Data Structures | Part 4(A)

Dictionaries (2) | lecture 17 part 5 | Rensselaer Polytechnic Institute

Dictionaries (2) | lecture 17 part 5 | Rensselaer Polytechnic Institute

Random Forests Bagging Boosting | Machine Learning Tutorial part 16

Random Forests Bagging Boosting | Machine Learning Tutorial part 16

Array based interview questions

Array based interview questions

Decision tree algorithm in machine learning python | machine learning Lecture 15

Decision tree algorithm in machine learning python | machine learning Lecture 15

Problem solving and design python | CS1 lecture 14 part 2| Rensselaer Polytechnic Institute

Problem solving and design python | CS1 lecture 14 part 2| Rensselaer Polytechnic Institute

Problem solving and design python | CS1 lecture 14 part 1| Rensselaer Polytechnic Institute

Problem solving and design python | CS1 lecture 14 part 1| Rensselaer Polytechnic Institute

Dynamic Arrays and Amortization Time Complexity| Array based questions part 2

Dynamic Arrays and Amortization Time Complexity| Array based questions part 2