Converting deep learning research papers to useful code

If deep learning is a super power, then turning theories from a paper to usable code is a hyper power

Why should I learn to implement machine learning research papers?

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.

How do I read and implement 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:

1. Collect 5–6 papers related to the specific topic(you can simply search arxiv or similar websites to get papers related to a topic).
2. Don’t read a single paper completely, instead skim through all of the papers and pick a paper that interests you or if you had a specific paper in mind, go pick it up, no one can stop you.
3. Read the abstract carefully and understand the idea from a high level and see whether your interest still persists, if so continue to skim through the images and see whether you can make assumptions on what the paper might be about.
4. Now read the introduction carefully line by line because most of what the paper contains will be explained here in the most simplest manner with minimal math.
5. If you wish, you can skip the math equations in the first pass, don’t skip the math if the Greek letters are familiar.
6. At any situation, if you get stuck or some words are confusing, never hesitate to google it. No one is born as master of everything ;)
7. After completing the first pass, you will be in a state where you understand the high level view of what the paper is trying prove or improve.
8. In the second pass, try to understand almost everything in the paper and if you encounter any pseudo-code, try to convert it into your python library of choice(PyTorch, TensorFlow…)
9. You can get more papers to read and get a better understanding of the field by going to the references section of each paper(same as connecting the dots).

💡 Some tips for effectively understanding a paper:

• If you are a beginner to reading research papers, it’s good to read some blog posts and videos related to that topic/research paper before reading the paper itself. This makes your job easier and you will not be discouraged by all that Greek letters.
• Always take notes and highlight important points in the research paper for easier reference while implementing the code for the paper.
• If you are new to implementing research papers and get stuck any where, it’s not a bad idea to go through open source implementations and see how others have done this.

#deep-learning #research #unsupervised-learning #machine-learning #deep learning

What is GEEK

Buddha Community

Converting deep learning research papers to useful code

If deep learning is a super power, then turning theories from a paper to usable code is a hyper power

Why should I learn to implement machine learning research papers?

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.

How do I read and implement 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:

1. Collect 5–6 papers related to the specific topic(you can simply search arxiv or similar websites to get papers related to a topic).
2. Don’t read a single paper completely, instead skim through all of the papers and pick a paper that interests you or if you had a specific paper in mind, go pick it up, no one can stop you.
3. Read the abstract carefully and understand the idea from a high level and see whether your interest still persists, if so continue to skim through the images and see whether you can make assumptions on what the paper might be about.
4. Now read the introduction carefully line by line because most of what the paper contains will be explained here in the most simplest manner with minimal math.
5. If you wish, you can skip the math equations in the first pass, don’t skip the math if the Greek letters are familiar.
6. At any situation, if you get stuck or some words are confusing, never hesitate to google it. No one is born as master of everything ;)
7. After completing the first pass, you will be in a state where you understand the high level view of what the paper is trying prove or improve.
8. In the second pass, try to understand almost everything in the paper and if you encounter any pseudo-code, try to convert it into your python library of choice(PyTorch, TensorFlow…)
9. You can get more papers to read and get a better understanding of the field by going to the references section of each paper(same as connecting the dots).

💡 Some tips for effectively understanding a paper:

• If you are a beginner to reading research papers, it’s good to read some blog posts and videos related to that topic/research paper before reading the paper itself. This makes your job easier and you will not be discouraged by all that Greek letters.
• Always take notes and highlight important points in the research paper for easier reference while implementing the code for the paper.
• If you are new to implementing research papers and get stuck any where, it’s not a bad idea to go through open source implementations and see how others have done this.

#deep-learning #research #unsupervised-learning #machine-learning #deep learning

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

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.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

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:

Also Read: Why Deep Learning DevCon Comes At The Right Time

---

Adversarial Robustness in Deep Learning

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.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

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.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

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.

---

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Learning to Code: How to Boost Up the Process?

Let’s face it: people are impatient by nature and most likely want things to happen faster in their lives. I would apply the same to code learners. Students, when starting to learn programming, first wonder how to speed up the training and make a career as a programmer as soon as possible.

I am not the one who convinces everyone that learning to program is a lightning-fast journey — the other thing is that it is not as difficult as people think. All boils down to interest, passion, regular practice, and patience, of course. I also often recommend different online and offline resources to my students to make their learning process easier, more effective, and faster. And in this post, I will share a few tips with you.

So, How to Learn Programming Languages Faster?

These tips are not magic pills and probably won’t make you a professional developer in a week, but they will definitely make your life easier, while the learning process won’t be that intimidating, vague, and boring. Besides, following all of them together may speed up training.

So, without any further delay, let’s get to them.

Consolidate Your Knowledge by Practicing More

No matter how simple a new subject is, it still requires you to consolidate your knowledge. Starting to play with code soon after you’ve completed the next section helps you learn the given concepts faster and feel confident when writing your first line of code. Fortunately, the web is full of platforms, where you can start practicing shortly.

Let’s consider them in detail.

#coding #learn-to-code #learning #programming #learning-to-code #machine-learning

Emojify - Create your own emoji with Deep Learning

Deep Learning project for beginners – Taking you closer to your Data Science dream

Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also lead to increasing data science research dedicated to emoji-driven storytelling.

With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. In this deep learning project, we will classify human facial expressions to filter and map corresponding emojis or avatars.

About the Dataset

The FER2013 dataset ( facial expression recognition) consists of 48*48 pixel grayscale face images. The images are centered and occupy an equal amount of space. This dataset consist of facial emotions of following categories:

• 0:angry
• 1:disgust
• 2:feat
• 3:happy
• 4:sad
• 5:surprise
• 6:natural

Download Dataset: Facial Expression Recognition Dataset

Download Project Code

Before proceeding ahead, please download the source code: Emoji Creator Project Source Code

Create your emoji with Deep Learning

We will build a deep learning model to classify facial expressions from the images. Then we will map the classified emotion to an emoji or an avatar.

Facial Emotion Recognition using CNN

In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images.

Download the dataset from the above link. Extract it in the data folder with separate train and test directories.

#python tutorials #create emoji with deep learning #deep learning project #deep learning project for beginners #deep learning project with source code