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Neural Networks for Everyone | ToolsInVision Learning Center

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MicroByTics | 82 videos | Updated 1 month ago
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Follow the complete course with transcripts, captions, visual summaries, and quick checks: www.toolsinvision.com/learning/neural-networks Learn neural networks through simple stories, familiar examples, and clear visual explanations—without advanced mathematics. This beginner-friendly course explains how neural networks process information, learn from mistakes, and make predictions. Each lesson introduces one important concept using engaging visuals designed for students, teenagers, and anyone curious about artificial intelligence. In this playlist, you will learn: • What a neural network is • How inputs, weights, bias, and artificial neurons work • Why neural networks use activation functions • How layers build increasingly meaningful features • How training, loss, and backpropagation improve predictions • The differences between classification, regression, and clustering No previous AI, programming, or mathematics experience is required. 🎓 Read the complete course: www.toolsinvision.com/learning/neural-networks 🌐 Explore ToolsInVision: www.toolsinvision.com New visual lessons about artificial intelligence, machine learning, deep learning, and computer vision are coming soon. #NeuralNetworks #ArtificialIntelligence #MachineLearning #DeepLearning #AIForBeginners #ToolsInVision

1:59

Build Your First Neural Network Project: The Complete Workflow. lesson 82

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1:57

AI Retraining Explained: Do New Labels Update Your Model? lesson 81

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1:54

Deploying AI: Why a Model Works Locally but Fails on Your Website. lesson 80

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1:56

GANs vs Diffusion: Why Realistic AI Images Are Not Evidence. lesson 79

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Autoencoders Explained: Can AI Spot a Defect? lesson 78

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Transformers Explained: Attention, Positions and Image Patches. lesson 77

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1:45

Attention Explained with One Simple Calculation. lesson 76

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LSTM vs GRU Explained: How Neural Networks Remember. lesson 75

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RNNs Explained: Why the Order of Your Data Matters. lesson 74

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1:47

Embeddings Explained: How AI Finds Similar Images. lesson 73

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1:51

Fine-Tuning Explained: When a Better Training Score Makes AI Worse. lesson 72

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1:47

Transfer Learning Explained: Train AI with Less Data. lesson 71

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2:40

YOLO vs Faster R-CNN: How Object Detectors Work. lesson 70

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mAP Explained: What Object Detection Scores Really Mean. lesson 69

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Precision vs Recall: Which AI Mistake Matters More? lesson 68

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NMS Explained: Why AI Produces Too Many Boxes. lesson 67

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Confidence Thresholds: Why AI Misses Objects. lesson 66

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IoU Explained: How Good Is Your Bounding Box? lesson 65

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Bounding Boxes Explained: How AI Marks What It Sees. lesson 64

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Object Detection Explained: How AI Finds Objects. Lesson 63

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2:55

SegNet vs U-Net: How Their Decoders Work | Neural Networks Lesson 62

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U-Net Explained: Encoder, Decoder, and Skip Connections | Neural Networks Lesson 61

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Transposed Convolution Explained Step by Step | Neural Networks Lesson 60

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Upsampling and Max Unpooling Explained | Neural Networks Lesson 59

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Fully Convolutional Networks (FCNs) Explained | Neural Networks Lesson 58

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Semantic Segmentation Explained: Pixel-by-Pixel Labels | Neural Networks Lesson 57

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AlexNet Explained: Large-Scale Image Classification | Neural Networks Lesson 56

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LeNet Explained: A Complete CNN Example | Neural Networks Lesson 55

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Fully Connected Layers, Logits, and Softmax | Neural Networks Lesson 54

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Flattening in CNNs: From Feature Maps to Vectors | Neural Networks Lesson 53

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Max Pooling vs Average Pooling | Neural Networks Lesson 52

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Max Pooling Explained Step by Step | Neural Networks Lesson 51

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CNN Filter Size and Receptive Fields | Neural Networks Lesson 50

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Same vs Valid Padding Explained | CNNs for Beginners — Lesson 49

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CNN Stride Explained: Calculate Feature Map Size | Lesson 48

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Feature Maps and Multiple Filters in CNNs | Lesson 47

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Convolution Step by Step: A Worked Example | Lesson 46

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CNN Filters and Convolution Explained | Lesson 45

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Local Connections and Weight Sharing in CNNs | Lesson 44

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How CNNs Build a Feature Hierarchy: From Edges to Objects | Lesson 43

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Spatial Structure in Images: Why Position Matters | Lesson 42

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2:19

Convolutional Neural Networks (CNNs) Explained | Lesson 41

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3:24

Vanishing, Exploding, and Dying Gradients | Neural Networks Lesson 40

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3:03

Batch Normalization Explained | Neural Networks Lesson 39

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Xavier vs He Initialization Explained | Neural Networks Lesson 38

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Why Weight Initialization Matters | Neural Networks Lesson 37

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Backpropagation Step by Step: A Worked Example | Neural Networks Lesson 36

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2:58

Partial Derivatives and the Chain Rule | Neural Networks Lesson 35

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Gradients and Derivatives Explained | Neural Networks Lesson 34

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2:53

What Backpropagation Actually Does | Neural Networks Lesson 33

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2:49

Bias vs. Variance: Finding the Right Balance | Lesson 32

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2:45

How to Diagnose High Variance and Overfitting | Lesson 31

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How to Diagnose High Bias and Underfitting | Lesson 30

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2:32

How to Read Learning Curves in Neural Networks | Lesson 29

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2:38

Image Normalization and Signal Smoothing Explained | Lesson 28

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2:34

Z-Score Normalization: Mean and Standard Deviation | Lesson 27

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3:02

Min-Max Normalization Explained Step by Step | Lesson 26

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2:40

Why Normalization Matters in Neural Networks | Lesson 25

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2:41

Bias and Variance Explained: Finding the Right Balance | Lesson 24

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Underfitting Explained: When a Neural Network Learns Too Little | Lesson 23

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Data Augmentation: Creating More Training Data from Existing Images | Lesson 22

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L2 Regularization: Keeping Neural Network Weights Under Control | Lesson 21

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L1 Regularization: Helping Neural Networks Remove Distractions | Lesson 20

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Dropout: Teaching Neural Networks to Become More Reliable | Lesson 19

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Overfitting: When a Neural Network Memorizes Too Much | Lesson 18

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Generalization: Can a Neural Network Handle New Data? | Lesson 17

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How a Neural Network Learns: The Complete Training Loop | Lesson 15

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Epochs, Batches, and Convergence Explained | Neural Networks Lesson 16

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Hinge Loss and the Safety Margin Explained | Neural Networks Lesson 14

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Categorical Cross-Entropy Explained with Fruit | Neural Networks Lesson 13

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Binary Cross-Entropy Explained Simply | Neural Networks Lesson 12

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Huber Loss Explained with a Parking Example | Neural Networks Lesson 11

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Mean Absolute Error Explained Simply | Neural Networks Lesson 10

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Why Squared Errors Change Machine Learning | Neural Networks Lesson 9

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Mean Squared Error Explained Step by Step | Neural Networks Lesson 8

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From Prediction to Loss: MSE, MAE & Huber Loss Explained | Neural Networks Lesson 7

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Classification vs Regression vs Clustering Explained Simply | Lesson 6

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How Does a Neural Network Learn? Training and Backpropagation Explained | Lesson 5

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Neural Network Layers and Deep Learning Explained Visually | Lesson 4

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Activation Functions Explained Simply | ReLU, Sigmoid, Tanh and Softmax | Lesson 3

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Inputs, Weights, Bias, and Neurons Explained Visually | Lesson 2

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2:28

What Is a Neural Network? A Simple Visual Explanation | Lesson 1

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