As we know, education in this digital age is available in many forms like videos, PDFs, e-books, and podcasts. Podcasts have become more popular among students or learners due to easy accessibility with electronic devices and the ability to make a good connection between the listener and speaker.
“The medium of podcasting and the personal nature of it, the relationship you build with your listeners and the relationship they have with you—they could be just sitting there, chuckling and listening…there’s nothing like that.”
– Marc Maron
In this article we are going to cover up top 12 podcasts that you should check out as a data scientist.
1. Machine Learning as a Software engineer
This conversation is with the Co-founder & CEO of paperspace, Dillion Erb. Paperspace provides GPU-enabled compute resources to data scientists and machine learning engineers. Dillion, explains how they build an organization which helps in building and scaling of machine learning workflows.
Host: Sam Charrington
Guest: Dillion Erb
2. NLP on COVID-19 and Mental Health
The pandemic(COVID-19) is going on and COVID-19 cases have skyrocketed since December 2019. The more the cases the more we will have data about the patients. In this podcast, Johannes Eichstaedt an assistant professor of psychology at Stanford University talking about the use of social media data such as Twitter and Facebook to understand the psychological behavior of a large populations and individuals.
Host: Sam Charrington
Guest: Johannes Eichstaedt
3. Neural Augmentation for Wireless Communication
In this podcast, Max Welling, a research chair in Machine Learning at the University of Amsterdam and a VP of Technologies at Qualcomm proposes a principle called Neural Augmentation.
In Neural Augmentation, we leverage the power of deep learning to learn the patterns that are impossible to detect by simple human observations. So we train a neural network to iteratively correct the classical solutions by using the three principles.
- Error Estimation
- MIMO demodulation
- Channel Estimation
You can learn more about the Neural Augmentation in this PDF.
Host: Sam Charrington
Guest: Max Welling
4. Quantum Machine Learning
Quantum machine learning is an intersection of Quantum Physics and Machine Learning.
A quantum computer uses qubits. Qubits are added with the ability to be put into superposition and share entanglement with one another.
By leveraging the techniques superposition and entanglement quantum computers can perform quantum operations that are difficult to process with the standard computers.
Host: Sam Charrington
Guest: Iordanis Kerenidis
5. Disrupting DeepFakes
Deepfake could generate realistic or convincing fake videos of a person saying or doing which may never happened in the real life. You can imagine or maybe not that how much chaos this can create.
In order to prevent malicious users from generating adversarial attacks against such image translation systems, which disrupt the resulting output image. This problem is called as Disrupting Deepfakes.
Host: Sam Charrington
Guest: Nataniel Ruiz
6. The intersection of AI and Computer Graphics
NVIDIA GPUs and deep learning trained a neural network to produce facial animations directly from the actor videos. It requires only five minutes of training data. The trained network generates all facial animation needed for the entire game from a video.
In this podcast, Aaron Lefohn who is a Senior Director of Real-Time Rendering Research at NVIDIA will talk about the how the power of AI can be harnessed to generate videos of facial animation from the actor videos.
Host: Noah Krevitz
Guest: Aaron Lefohn
7. Neural Networks, Mathematics & Teaching
Grant Sanderson is an educator on YouTube who makes visualization of mathematics which helps in understanding mathematics on a much deeper level.
In this podcast, Grant will give his insights about the collaboration between machine learning and mathematics. Grant will also tell how he was inspired by the teaching style of famous American Physicist Richard Feynman.
Host: Lex Fridman
Guest: Grant Sanderson
8. Getting Waymo into autonomous driving
Waymo is an autonomous driving technology development company. It is a subsidiary of Alphabet Inc, the parent company of Google. Waymo operates a commercial self-driving taxi service in Phoenix, Arizona called “Waymo One”.
9. Predicting Floods
With the help of AI, we can come up with a mathematical model to train with the past rainfall and water level data which can help us in preparing for the crisis to reduce it’s damage to the minimum.
In the following podcast, Sella Nevo who works at Google Research team working on a flood forecasting project. Sella will talk about the inundation model, the real-time water level measurements, elevation map creation, hydraulic modeling.
Hosts: Katheryn Corman & Neil Arms
Guest: Sella Nevo
10. Difference between artificial intelligence and ‘real’ intelligence
Intelligence can be called as mental ability for reasoning, problem solving or learning. Because it relies upon the cognitive functions of our brains. Now in this digital era, we have also coined a term called Artificial Intelligence, which is acquired by training the data with the help of mathematical models.
In this podcast, Andrew Busey an American Entrepreneur talks about the difference between the “real” intelligence and Artificial Intelligence.
Host: Byron Reese
Guest: Andrew Busey
11. A reality check on AI-driven medical assistants
The biomedical data has helped researchers to build mathematical models to automate the portions of healthcare process. For example, we can use computer vision to detect whether a patient suffering from pneumonia or not by giving input as an X-ray image.
In this podcast, Katie Malone who is a data scientist in the research and development department at Civic Analytics talks about algorithms like computer vision, one that diagnoses diabetic retinopathy, and another that classifies liver cancer.
Host: Ben Jaffe
Guest: Katie Malone
12. Criminology and data science
As technology is growing the more our cities are getting harnessed with high-tech security. This provides cities with sources of real-time information that is happening throughout the day in a city.
For example, if we train a machine learning model on a chain snatcher videos then we can run the inference with the model on the real-time environment to detect whether someone’s chain is being snatched or not.
Host: Katie Malone
Guest: Zach Drake
Well after all these podcasts you might have or certainly become somewhat interested in learning Data Science. So I am listing out the resources where you can learn machine learning for free.
AI is becoming the new oil for many businesses and what we cover was just the tip of the iceberg,so you better harness yourself with the knowledge of the AI spells to make this world a better place. 🙂
Where Can You Learn About MLOps? What Are the Best Books, Articles, or Podcasts to Learn MLOps?ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It
4 mins read | Paweł Kijko | Updated May 31st, 2021
MLOps is not a piece of cake. Especially in today’s changing environment. There are many challenges—construction, integrating, testing, releasing, deployment, and infrastructure management. You need to follow good practices and know how to adjust to the challenges.
And if you don’t learn and develop your knowledge, you’ll fall out of the loop. The right resources can help you follow the best practices, discover helpful tips, and learn about the latest trends.
You don’t have to look far, we’ve got you covered! Here’s your list of the best go-to resources about MLOps—books, articles, podcasts, and more. Let’s dive in!
1. Introducing MLOps from O’Reilly
Introducing MLOps: How to Scale Machine Learning in the Enterprise is a book written by Mark Treveil and the Dataiku Team (collective authors). It introduces the key concepts of MLOps, shows how to maintain and improve ML models over time, and tackles the challenges of MLOps.
The book was written specifically for analytics and IT operations team managers—the people directly facing the task of scaling machine learning (ML) in production. It’s a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.
The book is divided into three parts:
- An introduction to the topic of MLOps, how and why it has developed as a discipline, who needs to be involved to execute MLOps successfully, and what components are required.
- The second part follows the machine learning model life cycle, with chapters on developing models, preparing for production, deploying to production, monitoring, and governance.
- Provides tangible examples of how MLOps looks in companies today, so readers can understand the setup and implications in practice.