Resource of free step by step video how to guides to get you started with machine learning.
Wednesday, April 3, 2024
Build an AI Digit Classifier! | Train a Feedforward Neural Network with Python & MNIST Dataset
Unlock the power of Artificial Intelligence! In this video, we build a handwritten digit classifier using a Feedforward Neural Network (FNN) and the classic MNIST dataset. Learn how to: * Understand FNNs and MNIST * Code your own FNN in Python with TensorFlow & Keras * Train the model to recognize handwritten digits * Evaluate its performance (accuracy, precision, recall) Perfect for beginners interested in AI and Machine Learning! Bonus: Clear explanations, step-by-step coding walkthrough, and awesome visuals! ✨ 📝 *Resources:* - Code snippets: https://github.com/niigyanchristian/Machine-Learning-Tutorial 🔍 Chapters: 0:00 - Intro 0:11 - How Feedforward Neural Networks 1:30 - Import Libraries 1:45 - Load Dataset 4:23 - Normalize pixel values 5:45 - Flatten Images 7:45 - Define architecture 10:42 - Compile Model 12:10 - Train Model 14:00 - Evaluate Model 15:40 - Recap 16:55 - Outro Activation Functions in machine learning: https://www.youtube.com/watch?v=Nl3RFjwckeQ Neural Networks: https://www.youtube.com/watch?v=YnBspoENV2s Mahcine Learning Playlist: https://www.youtube.com/playlist?list=PLXUAh2kKFjXsEWU1jE-qMPvU4n1IMaArj React Native Playlist: https://www.youtube.com/playlist?list=PLXUAh2kKFjXupHedKgytiVeUy6I-9SP_7 HTML Tutorial for Beginners Playlist: https://www.youtube.com/playlist?list=PLXUAh2kKFjXt4kYB8lE7wT7hvsUfoDnXf Git and GitHub Playlist: https://www.youtube.com/watch?v=gZWL2K49oTA&list=PLXUAh2kKFjXtkpwJLGKlNxUWvWlYINan5 NodeJs Todo App Platlist: https://youtube.com/playlist?list=PLXUAh2kKFjXsr6pA4_3CDnfBD0souzGU2 #machinelearning #artificialintelligence #python #tensorflow #keras #mnist #fnn #digitrecognition
Labels:
ai podcast,
ArtificialIntelligence,
Classifier,
convolutional neural network,
deep learning,
digitrecognition,
Machine Learning,
machinelearning,
TensorFlow,
what is machine learning
Published by Free artificial intelligence and machine learning video tutorial resource
Related video tutorials
Subscribe to:
Post Comments (Atom)
Most watched
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Quick answer: For machine-learning work, buy an external SSD over a spinning hard drive, pick USB 3.2 Gen 2 (10 Gbps) or faster, and size f...
-
Quick answer: Yes, for most budget builders: a used RTX 3090 is still the cheapest route to 24 GB of VRAM with full CUDA support. Capacity...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: The cheapest path to serious local-AI VRAM is not one new flagship card — it is two used ones. A single RTX 5090 costs $1,999...
-
Quick answer: FLUX.1 dev needs about 10 GB of VRAM at Q4/NF4 quantization for 1024×1024 image generation, so a 12 GB GPU such as the GeFor...
No comments:
Post a Comment