This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.
$ git clone https://github.com/yunjey/pytorch-tutorial.git $ cd pytorch-tutorial/tutorials/PATH_TO_PROJECT $ python main.py
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Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc
Python, Numpy, Pandas and Matplotlib
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
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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If deep learning is a super power, then turning theories from a paper to usable code is a hyper power
As I’ve said, being able to convert a paper to code is definitely a hyper power, especially in a field like machine learning which is moving faster and faster each day.
Most research papers come from people within giant tech companies or universities who may be PhD holders or the ones who are working on the cutting edge technologies.
What else can be more cool than being able to reproduce the research done by these top notch professionals. Another thing to note is that the ones who can reproduce research papers as code is in huge demand.
Once you get the knack of implementing research papers, you will be in a state on par with these researchers.
These researchers too has acquired these skills through the practice of reading and implementing research papers.
You might say, “Hm, I have a general understanding of the deep learning algorithms like fully connected networks, convolutional neural networks, recurrent neural networks, but the problem is that I would like to develop SOTA(state of the art) voice cloning AI but I know nothing about voice cloning :( ”.
Okay, here is your answer(some parts of my method is taken from Andrew Ng’s advice on reading papers).
If you want to learn about a specific topic:
💡 Some tips for effectively understanding a paper:
#deep-learning #research #unsupervised-learning #machine-learning #deep learning
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.
#importing the libraries import torch import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
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