filmov
tv
Machine Learning Databases and How to Access them with Pytorch - MNIST Tutorial
Показать описание
In this tutorial, we will talk about popular Machine Learning databases and how we can easily access them with Pytorch. 🔥🔥🔥
In particular, we will focus on MNIST, which is a handwritten digits database with 70,000 different images. We will load it with a very simple Pytorch command and we will have a closer look at its content, as well as its feature + label structure.
We will also discuss data transforms, why we need them and how do we decide which transforms work better for what kind of data.
Other computer vision databases we will briefly discuss are CIFAR-10, FashionMNIST and HMDB51. You can find the full list of available databases and transforms at the very bottom of the description ⬇⬇⬇
Have you seen the previous ML tutorial I refer to in this video?
New to Google Colab?
In my following video, I show you how to set it up for the first time:
**************************************
⏰Time Stamps ⏰
00:00 - intro
00:23 - load MNIST with Pytorch
02:01 - MNIST features and targets
03:20 - Pytorch Databases
04:14 - data transforms
07:55 - in the next tutorial
**************************************
⭐ IMPORTS ⭐
(starter code for the entire project- not just this video)
**************************************
import torch
from torch import nn, optim
from torchvision import datasets, transforms, models
**************************************
⭐ INSTALL DEPENDENCIES ⭐
⭐ ANACONDA - RECOMMENDED! ⭐
conda install -c pytorch pytorch
conda install -c pytorch torchvision
**************************************
⭐ INSTALL DEPENDENCIES ⭐
⭐ PYPI - ONLY IF THERE'S NO OTHER CHOICE! ⭐
pip install torch
pip install torchvision
**************************************
⭐ IMPORTANT LINKS ⭐
🤩 All Available Torchvision Datasets:
🤩 All Available Pytorch Transforms:
🤩 Complete Colab Notebook for loading MNIST, FashionMNIST and CIFAR-10:
🤩 Complete Notebook for Next Lesson (Neural Network Training on MNIST Data):
* Please keep in mind that the next lessons notebook doesn't include testing and validation!!
**************************************
🔊 My apologies for the sound quality 🔊
I'm trying different recording solutions and this one is definitely NOT THE ONE! 😅
In particular, we will focus on MNIST, which is a handwritten digits database with 70,000 different images. We will load it with a very simple Pytorch command and we will have a closer look at its content, as well as its feature + label structure.
We will also discuss data transforms, why we need them and how do we decide which transforms work better for what kind of data.
Other computer vision databases we will briefly discuss are CIFAR-10, FashionMNIST and HMDB51. You can find the full list of available databases and transforms at the very bottom of the description ⬇⬇⬇
Have you seen the previous ML tutorial I refer to in this video?
New to Google Colab?
In my following video, I show you how to set it up for the first time:
**************************************
⏰Time Stamps ⏰
00:00 - intro
00:23 - load MNIST with Pytorch
02:01 - MNIST features and targets
03:20 - Pytorch Databases
04:14 - data transforms
07:55 - in the next tutorial
**************************************
⭐ IMPORTS ⭐
(starter code for the entire project- not just this video)
**************************************
import torch
from torch import nn, optim
from torchvision import datasets, transforms, models
**************************************
⭐ INSTALL DEPENDENCIES ⭐
⭐ ANACONDA - RECOMMENDED! ⭐
conda install -c pytorch pytorch
conda install -c pytorch torchvision
**************************************
⭐ INSTALL DEPENDENCIES ⭐
⭐ PYPI - ONLY IF THERE'S NO OTHER CHOICE! ⭐
pip install torch
pip install torchvision
**************************************
⭐ IMPORTANT LINKS ⭐
🤩 All Available Torchvision Datasets:
🤩 All Available Pytorch Transforms:
🤩 Complete Colab Notebook for loading MNIST, FashionMNIST and CIFAR-10:
🤩 Complete Notebook for Next Lesson (Neural Network Training on MNIST Data):
* Please keep in mind that the next lessons notebook doesn't include testing and validation!!
**************************************
🔊 My apologies for the sound quality 🔊
I'm trying different recording solutions and this one is definitely NOT THE ONE! 😅
0:07:52
Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2024 | Simplilearn
0:02:35
Machine Learning Explained in 100 Seconds
0:05:25
From 0 to Machine Learning Engineer in 12 Months: The Complete Roadmap
0:03:22
Vector databases are so hot right now. WTF are they?
0:00:28
Learning Machine Learning has never been easier #shorts #machinelearning #statistics #datascience
0:38:16
Lecture: Mathematics of Big Data and Machine Learning
0:01:57
Build AI into Your Database. The fastest and simplest way to deploy applied Machine Learning
0:13:44
MLOps explained | Machine Learning Essentials
0:12:20
Top Vector Databases for AI: Weaviate vs Milvus vs pgVector and More
0:13:55
Add ML to your applications via simple database queries
0:13:57
How is data prepared for machine learning?
0:09:45
AI VS ML VS DL VS Data Science
0:08:23
Machine Learning Databases and How to Access them with Pytorch - MNIST Tutorial
0:05:22
Database vs Data Warehouse vs Data Lake | What is the Difference?
0:08:42
15 futuristic databases you’ve never heard of
0:49:43
Python Machine Learning Tutorial (Data Science)
0:04:23
Vector Databases simply explained! (Embeddings & Indexes)
0:53:40
Database Driven Machine Learning
3:10:19
SQL Tutorial for Beginners [Full Course]
0:22:26
Complete Roadmap for Machine Learning | ML Roadmap for Beginners
0:05:12
Big Data In 5 Minutes | What Is Big Data?| Big Data Analytics | Big Data Tutorial | Simplilearn
0:00:55
Vector Databases (Pinecone)
1:42:54
Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
0:05:52
Deep Learning | What is Deep Learning? | Deep Learning Tutorial For Beginners | 2023 | Simplilearn
Комментарии