1594211014
Hands-On Guide to Implement Deep Autoencoder in PyTorch
Artificial Neural Networks have many popular variants that are applied in supervised and unsupervised learning problems. The Autoeconders are also a variant of neural networks that are mostly applied in unsupervised learning problems. When they come with multiple hidden layers in the architecture, they are referred to as the Deep Autoencoders. These models can be applied in a variety of applications including image reconstruction. In image reconstruction, they learn the representation of the input image pattern and reconstruct the new images matching to the original input image pattern. Image reconstruction has many important applications especially in the medical field where the decoded and noise-free images are required from the available incomplete or noisy images.
#artificial-intelligence #neuralnetworks #pytorch
What is GEEK
Buddha Community
1600632000
PyTorch For Deep Learning
What is Pytorch ?
Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc
Prerequisites
Python, Numpy, Pandas and Matplotlib
Tensor Basics
What is a tensor ?
A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.
#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning
1602002446
Hand Sanitizer in bulk - Get your effective hand sanitizer here
With the spread of various harmful virus globally causing immense distress and fatalities to human mankind, it has become absolutely essential for people to ensure proper and acute hygiene and cleanliness is maintained. To further add to the perennial hardship to save lives of people the recent pandemic of Covid-19 affected globally created the worst nightmare for people of all walks of life. Looking at the present crisis, it has become imperative for human beings to be encouraged to tackle this challenge with an everlasting strength to help protect oneself and their loved ones against the devastating effects of the virus. One thing that stands up between keeping all safe and vulnerable is by making sure that everybody attentively Hand wash periodically to help physically remove germs from the skin and getting rid of the live microbes.
The essence of apposite handwashing is based around time invested in washing and the amount of soap and water used. Technically, washing hands without soap is much less effective anyway. But incase a proper handwashing support system doesn’t become possible around, the usage of Effective Hand Sanitizer will certainly help fight to reduce the number of microbes on the surface of hands efficiently, eliminating most variants of harmful bacteria to settle.
The need has come about for Hand Sanitizer in bulk to save your daily life aptly maintaining a minimum of 60% alcohol - as per the CDC recommendations and approved by USFDA for its greater effectiveness. With the growing demand of people on the move the demand for easy to carry, small, and travel size worthy pouches that are also refillable once the product runs out is the need of the hour. To further make sure that human lives are well protected from these external viruses, it is mandatory for producer of effective Hand Sanitizer to evolve products circumspectly with ingredients that produce not just saving lives but with multiple benefits for people of all ages.
#hand sanitizer #hand sanitizer in bulk #hand sanitizer ingredient #hand sanitizer to alcohol #hand sanitizer travel size #hand sanitizer wholesale
1594211014
Hands-On Guide to Implement Deep Autoencoder in PyTorch
Artificial Neural Networks have many popular variants that are applied in supervised and unsupervised learning problems. The Autoeconders are also a variant of neural networks that are mostly applied in unsupervised learning problems. When they come with multiple hidden layers in the architecture, they are referred to as the Deep Autoencoders. These models can be applied in a variety of applications including image reconstruction. In image reconstruction, they learn the representation of the input image pattern and reconstruct the new images matching to the original input image pattern. Image reconstruction has many important applications especially in the medical field where the decoded and noise-free images are required from the available incomplete or noisy images.
#artificial-intelligence #neuralnetworks #pytorch
1618464780
Hands-On Guide to Torch-Points3D: A Modular Deep Learning Framework for 3D Data
There has been a surge of advancements in automated analysis of 3D data caused by affordable LiDAR sensors, more efficient photogrammetry algorithms, and new neural network architectures. So much that the number of papers related to 3D data being presented at vision conferences is now on par with images, although this rapid methodological development is beneficial to the young field of deep learning for 3D, with its fast pace come several shortcomings:
- Adding new datasets, tasks, or neural architectures to existing approaches is a complicated endeavour, sometimes equivalent to reimplementing from scratch.
- Handling large 3D datasets requires a significant time investment and is prone to many implementation pitfalls.
- There is no standard approach for inference schemes and performance metrics, which makes assessing and reproducing new algorithms’ intrinsic performance difficult.
#developers corner #3d data #deep learning #deep learning frameworks #exploring 3d data in ai #kpconv #point cloud data #python libraries #pytorch 3d #pytorch geometric #torch-points3d
1600642800
PyTorch For Deep Learning — Confusion Matrix
Note: This is a regular classification problem with PyTorch and this is exactly like the one in the previous post of the “PyTorch for Deep Learning” series.
The Reason for doing writing the post is for some more reference to classification problem and better understanding. If You are already good enough with classification withneural network, skip to the part where confusion matrix comes in.
Jumping to the Code Part
- Importing required libraries
#importing the libraries
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
2. Data
The dataset is available at kaggle : https://www.kaggle.com/dragonheir/logistic-regression
#importing the dataset
df = pd.read_csv('Social_Network_Ads.csv')
df.head()
#pytorch-tutorial #confusion-matrix #deep-learning #deep-learning-course #pytorch