TorchSketch is developed based on Python 3.7.
To avoid any conflicts with your existing Python setup, it’s better to install TorchSketch into a standalone environment, e.g., an Anaconda virtual environment.
Assume that you have installed Anaconda. Please create a virtual environment before installation of TorchSketch, as follows.
We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.
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So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some regression algorithms, classification algorithms and algorithms that can be used for both types of problems (SVM**, **Decision Trees and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for clustering and learned about several clustering techniques.
We also talked about how to quantify machine learning model performance and how to improve it with regularization. In all these articles, we used Python for “from the scratch” implementations and libraries like TensorFlow, Pytorch and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.
In general, every machine learning algorithm is composed of three integral parts:
As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical explanations of why and how these algorithms work were done later. Some of these algorithms are Decision Trees and kNN. Other algorithms, which were developed later had this thing in mind beforehand. SVMis one example.
During the training, we change the parameters of our machine learning model to try and minimize the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use optimizers. They put all different parts of the machine learning algorithm together. So far we mentioned Gradient Decent as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the grandfather of all optimization techniques and its variation. Note that these techniques are not machine learning algorithms. They are solvers of minimization problems in which the function to minimize has a gradient in most points of its domain.
Data that we use in this article is the famous Boston Housing Dataset . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small dataset with only 506 samples.
For the purpose of this article, make sure that you have installed the following _Python _libraries:
Once installed make sure that you have imported all the necessary modules that are used in this tutorial.
import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler from sklearn.linear_model import SGDRegressor
Note that we also use simple Linear Regression in all examples. Due to the fact that we explore optimizationtechniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression here. As a quick reminder the formula for linear regression goes like this:
where w and b are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the w and b, so we get the desired output from the machine learning model. This means that we are trying to make the value of our error vector as small as possible, i.e. to find a global minimum of the cost function.
One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.
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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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Open source today is a word that often include a lot of things, such as open knowledge (Wikimedia projects), open hardware (Arduino, Raspberry Pi), open formats (ODT/ODS/ODP) and so on.
It is a world of opportunities that can be difficult for newcomers but also for intermediates. This article will help you discover how to approach specific roles, activities or projects/communities in the best way.
I decided to write a book in my personal style about my experience in the last 7 to 8 years in open source. I was surprised when I reached 100 pages about various different topics.
My idea was to write something that I would like to read, so nothing that is boring or complicated, but full of real facts.
The second goal was to include my experience but also my philosophy on contributing and how I contribute daily.
Thirdly, I wanted to give a lot of hints and resources and an overall view of this open source world.
Basically, I wanted to write something different from self-help or coaching books that includes just a list of suggestions and best practices. Instead, I take real examples from real life about the OSS world.
As a contributor and developer, I prefer to have real cases to study, because best practices are useful, but we need to learn from others and this world is full of good and bad cases to discover.
In 2019, I started writing a book after Fosdem 2019 and after 2 years inside the Mozilla Reps Council. In that Fosdem edition, I had a talk “Coaching for Open Source Communities 2.0” and after the feedback at the conference and my thoughts in various roles, activities, and projects, it was time to write something.
At the end it wasn’t a manual but a book that included my experience, learnings, best practices and so on in Localization, Development, Project Maintainer, Sysadmin, Community Management, Mentor, Speaker and so on. It contains the following sections:
There are also three appendices that are manuals which I wrote throughout the years and gathered and improved for this book. They are about: community management, public speaking, and mentoring.
The book ends with my point of view about the future and what we have to do to change opinions about those topics.
I wrote this book and published in October 2019, but it was only possible with the help of reviews and localizers that improved and contributed. Yes, because this book is open source and free for everyone.
I picked the GPL license because this license changed the world and my life in the best way. Using this license is just a tribute. This decision usually is not clear because after all this is a book and there are better licenses like Creative Commons.
#open-source #contributing-to-open-source #programming #software-development #development #coding #books #open-source-software
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