Скачать или смотреть Beginner to Pro: Building a Convolution Neural Network | FashionMNIST Tutorial | Computer Vision

In this video, we take a deep dive into Convolutional Neural Networks (CNNs) to solve a real-world computer vision problem using the FashionMNIST dataset.

This is a continuation of our deep learning series where we previously built two underperforming models. Now, we finally bring it all together by building a powerful CNN that significantly improves accuracy and performance.

Timestamps:
0:00 - Intro + CNN & Pooling Theory Explained
12:27 - CNN & Pooling in Code (Practical Walkthrough)
22:17 - Building the CNN Architecture (Step-by-Step)
40:22 - Training the CNN Model on FashionMNIST
48:22 - Evaluating Model Performance (Accuracy & Loss)
52:09 - Visualizing Model Predictions & Results
58:21 - Making Real-Time Predictions with the Trained CNN

What You'll Learn:
Theory behind CNNs (with intuition)
Convolution, Pooling, Flattening explained visually
Implementing CNN from scratch using PyTorch
Evaluating and improving model performance
Why CNNs outperform traditional neural networks for image tasks

Link to Google Colab: https://colab.research.google.com/dri...

Perfect for beginners, students, and enthusiasts looking to break into computer vision and deep learning.

Dataset Used: FashionMNIST
Tools: Python, PyTorch
Level: Beginner to Intermediate

Watch the previous videos in the playlist for full understanding:
Link to the Playlist:    • Computer Vision with PyTorch  

Latest playlist video:    • A Beginner's Guide to Neural Network Activ...  

Got questions? Drop them in the comments — I respond to every one!

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#cnn #deeplearning #fashionmnist #computervision #machinelearning