4.5 How Neural Networks Learn: The Magic of Backpropagation
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4.5 How Neural Networks Learn: The Magic of Backpropagation
3:42
GPT-OSS-20b Runned on 8Gb VRAM Windows 11
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GPT-OSS-20b Runned on 8Gb VRAM Windows 11
0:27
4.4 Activation Functions: Bringing Your Neural Network to Life
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4.4 Activation Functions: Bringing Your Neural Network to Life
3:01
3.23 What is a "Model" in Machine Learning? (Clear Explanation)
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3.23 What is a "Model" in Machine Learning? (Clear Explanation)
1:57
4.3 Understanding Neural Networks: Layers, Neurons, and Connections Explained
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4.3 Understanding Neural Networks: Layers, Neurons, and Connections Explained
3:28
3.22 Introduction to Scikit-Learn: Building Your First Machine Learning Model
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3.22 Introduction to Scikit-Learn: Building Your First Machine Learning Model
2:09
4.2 The Perceptron: The Simplest Neural Network & AI's Historic First Step
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4.2 The Perceptron: The Simplest Neural Network & AI's Historic First Step
3:16
3.21 K-Nearest Neighbors (KNN): The Intuitive Algorithm That Simply Works
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3.21 K-Nearest Neighbors (KNN): The Intuitive Algorithm That Simply Works
2:29
4.1 Neural Networks Explained: Inspired by the Human Brain
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4.1 Neural Networks Explained: Inspired by the Human Brain
3:34
3.20 The Confusion Matrix Explained: Your Complete Guide to Model Evaluation
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3.20 The Confusion Matrix Explained: Your Complete Guide to Model Evaluation
2:29
3.19 Evaluating Your Model: Beyond Accuracy with Precision & Recall
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3.19 Evaluating Your Model: Beyond Accuracy with Precision & Recall
2:03
3.18 K-Means Clustering: The Ultimate Guide to Unsupervised Learning
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3.18 K-Means Clustering: The Ultimate Guide to Unsupervised Learning
2:37
3.17 Support Vector Machines (SVMs) Explained: A Visual Guide to Classification
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3.17 Support Vector Machines (SVMs) Explained: A Visual Guide to Classification
2:26
3.16 Random Forests Explained: The Power of Ensemble Learning in ML
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3.16 Random Forests Explained: The Power of Ensemble Learning in ML
2:27
3.15 Decision Trees Explained: How They Work & Build Intuitive ML Models
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3.15 Decision Trees Explained: How They Work & Build Intuitive ML Models
2:20
3.14 Logistic Regression: From Lines to Probabilities | Machine Learning Explained
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3.14 Logistic Regression: From Lines to Probabilities | Machine Learning Explained
2:19
3.13 Linear Regression: Your First ML Algorithm for Predicting Continuous Values
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3.13 Linear Regression: Your First ML Algorithm for Predicting Continuous Values
2:20
3.12 Bias-Variance Tradeoff: Core ML Concept Explained
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3.12 Bias-Variance Tradeoff: Core ML Concept Explained
2:15
Overfitting vs. Underfitting: Finding the Balance in Machine Learning | Expert Interview
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Overfitting vs. Underfitting: Finding the Balance in Machine Learning | Expert Interview
4:58
3.11 Overfitting vs. Underfitting: Finding the Perfect Balance in ML
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3.11 Overfitting vs. Underfitting: Finding the Perfect Balance in ML
2:20
3.10 Training, Validation & Test Sets: The Key to Proper ML Model Evaluation
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3.10 Training, Validation & Test Sets: The Key to Proper ML Model Evaluation
2:20
3.9 Feature Engineering: The Art of Creating Powerful ML Features
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3.9 Feature Engineering: The Art of Creating Powerful ML Features
2:04
3.8 Handling Missing Data: Essential Techniques for Machine Learning
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3.8 Handling Missing Data: Essential Techniques for Machine Learning
2:02
3.7 Data Preprocessing: The Critical First Step in Machine Learning
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3.7 Data Preprocessing: The Critical First Step in Machine Learning
1:51
3.6 Machine Learning Workflow: The Complete Step-by-Step Guide
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3.6 Machine Learning Workflow: The Complete Step-by-Step Guide
2:15
3.5 How to Frame Problems for Machine Learning: The 4-Step Blueprint
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3.5 How to Frame Problems for Machine Learning: The 4-Step Blueprint
2:10
3.4 Reinforcement Learning Explained: AI Through Trial & Error
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3.4 Reinforcement Learning Explained: AI Through Trial & Error
2:21
3.3 Unsupervised Learning Explained: Finding Hidden Patterns in Data
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3.3 Unsupervised Learning Explained: Finding Hidden Patterns in Data
1:59
3.2 Supervised Learning Explained: Learning with Labels
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3.2 Supervised Learning Explained: Learning with Labels
1:57
2.25 Essential Python Libraries for AI: NumPy, Pandas & More
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2.25 Essential Python Libraries for AI: NumPy, Pandas & More
2:26
2.24 The Math Behind Linear Regression: Putting AI Concepts Together
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2.24 The Math Behind Linear Regression: Putting AI Concepts Together
2:42
2.23 Gradient Descent Visually Explained: How AI Actually Learns
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2.23 Gradient Descent Visually Explained: How AI Actually Learns
2:40
2.22 Loss Functions Explained: How AI Measures Errors
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2.22 Loss Functions Explained: How AI Measures Errors
2:14
2.21 Activation Functions Explained: Math Behind Neural Networks
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2.21 Activation Functions Explained: Math Behind Neural Networks
2:28
2.20: The Math of a Neuron: What Happens Inside an AI's Building Block
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2.20: The Math of a Neuron: What Happens Inside an AI's Building Block
2:26
2.19 PCA Explained Visually: Taming the Curse of Dimensionality with Math
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2.19 PCA Explained Visually: Taming the Curse of Dimensionality with Math
2:32
2.18 Eigenvectors & Eigenvalues Made Simple: The Math Behind PCA and Google's Algorithm
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2.18 Eigenvectors & Eigenvalues Made Simple: The Math Behind PCA and Google's Algorithm
2:27
2.17 Graph Theory in AI: How Neural Networks Map Relationships & Connections
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2.17 Graph Theory in AI: How Neural Networks Map Relationships & Connections
2:34
2.16 Set Theory for Data Science: The Hidden Foundation of Data Organization
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2.16 Set Theory for Data Science: The Hidden Foundation of Data Organization
2:18
2.15 Logarithms & Exponents in AI: The Powerful Partnership Powering Algorithms
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2.15 Logarithms & Exponents in AI: The Powerful Partnership Powering Algorithms
2:26
2.14 What is a Function in AI? From Simple Rules to Neural Networks Explained
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2.14 What is a Function in AI? From Simple Rules to Neural Networks Explained
2:28
2.13 Probability Distributions Every AI Learner Should Know
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2.13 Probability Distributions Every AI Learner Should Know
2:23
2.12 Standard Deviation & Variance – Understanding Data Spread in AI
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2.12 Standard Deviation & Variance – Understanding Data Spread in AI
2:35
2.11 Mean, Median, and Mode Explained – AI Statistics for Beginners
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2.11 Mean, Median, and Mode Explained – AI Statistics for Beginners
2:31
2.10 Descriptive vs. Inferential Statistics in AI – Simple Guide for Beginners
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2.10 Descriptive vs. Inferential Statistics in AI – Simple Guide for Beginners
2:41
2.9 Statistics for AI: From Basics to Key Concepts
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2.9 Statistics for AI: From Basics to Key Concepts
2:28
2.8 Bayes' Theorem Explained with Simple Examples
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2.8 Bayes' Theorem Explained with Simple Examples
2:48
2.7 Probability Theory for AI: Understanding Uncertainty
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2.7 Probability Theory for AI: Understanding Uncertainty
2:25
2.6 The Chain Rule: The Engine of Backpropagation
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2.6 The Chain Rule: The Engine of Backpropagation
2:32
2.5 Calculus for AI: Grasping Gradients & Optimization
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2.5 Calculus for AI: Grasping Gradients & Optimization
2:52
2.4 Understanding Tensors for Deep Learning
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2.4 Understanding Tensors for Deep Learning
2:23
2.3 Vectors and Matrices: The Building Blocks of Data
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2.3 Vectors and Matrices: The Building Blocks of Data
2:28
2.2 Linear Algebra for AI: A Visual Introduction
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2.2 Linear Algebra for AI: A Visual Introduction
2:15
3.1 What is Machine Learning? A Practical Introduction.
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3.1 What is Machine Learning? A Practical Introduction.
2:31
2.1 Why Math is Crucial for AI
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2.1 Why Math is Crucial for AI
2:52
1.25 Your First AI "Hello World"
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1.25 Your First AI "Hello World"
2:33
1.24 How Search Engines Work
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1.24 How Search Engines Work
2:40
1.23 AI and Robotics
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1.23 AI and Robotics
2:40
1.22 How to Think Like an AI Engineer
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1.22 How to Think Like an AI Engineer
2:36
1.21 The Future of AI
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1.21 The Future of AI
2:35
1.20 Why is AI So Important Today?
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1.20 Why is AI So Important Today?
2:29
1.19 The Role of Data in AI
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1.19 The Role of Data in AI
2:34
1.18 AI for Absolute Beginners
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1.18 AI for Absolute Beginners
2:42
1.17 Common AI Myths Debunked
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1.17 Common AI Myths Debunked
2:59
1.16 The AI Hype Cycle
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1.16 The AI Hype Cycle
2:58
1.15: Getting Started with AI: Your First 5 Steps
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1.15: Getting Started with AI: Your First 5 Steps
2:57
1.14: AI's Impact on the Job Market
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1.14: AI's Impact on the Job Market
2:50
1.13: AI's Impact on Industries
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1.13: AI's Impact on Industries
2:51
1.12 Symbolic vs. Connectionist AI
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1.12 Symbolic vs. Connectionist AI
2:44
1.11 The Philosophy of AI
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1.11 The Philosophy of AI
3:03
1.10 The Turing Test - Can Machines Think?
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1.10 The Turing Test - Can Machines Think?
2:56
1.9 The 4 Main Goals of Artificial Intelligence
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1.9 The 4 Main Goals of Artificial Intelligence
2:44
1.8 AI in Everyday Life: You're Using It More Than You Think!
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1.8 AI in Everyday Life: You're Using It More Than You Think!
2:21
1.7 The Pioneers of AI
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1.7 The Pioneers of AI
2:26
1.6 Key Terminology in AI You Need to Know
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1.6 Key Terminology in AI You Need to Know
3:07
1.5 AI vs. ML vs. Deep Learning
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1.5 AI vs. ML vs. Deep Learning
3:20
1.4 How Does AI Actually Work?
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1.4 How Does AI Actually Work?
3:58
1.3 Types of AI: Narrow, General, & Superintelligence
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1.3 Types of AI: Narrow, General, & Superintelligence
3:28
1.2 The History of AI: From Turing to Today
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1.2 The History of AI: From Turing to Today
3:52
1.1 What is Artificial Intelligence? – AI Explained for Absolute Beginners
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1.1 What is Artificial Intelligence? – AI Explained for Absolute Beginners
4:13
Key NLP Techniques - Essential Methods for Text Analysis
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Key NLP Techniques - Essential Methods for Text Analysis
11:26
Keras vs Other Frameworks - When to Choose Keras for Deep Learning
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Keras vs Other Frameworks - When to Choose Keras for Deep Learning
6:37
SQL vs NoSQL - Choosing the Right Database for AI
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SQL vs NoSQL - Choosing the Right Database for AI
5:06
ClearML - The Complete ML Platform
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ClearML - The Complete ML Platform
10:01
Tensorflow VS PyTorch - Key Differences Between Deep Learning Frameworks
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Tensorflow VS PyTorch - Key Differences Between Deep Learning Frameworks
3:21