We have a new panelist! Plus, Edward Raff joins the Adventure to discuss his new book Inside Machine Learning. He walks us through Convolutional Neural Networks and then talks us through to build, train, and use them to solve problems through Machine Learning.

Charles Max Wood goes into the origin story of his podcasting career and how it relates to his programming career. He starts with his interest from a young age in technology and his dreams of being a radio DJ. He moves quickly through college and into his first job after college where he was introduced to podcasts by a co-worker who had purchased an iPod. He calls out several mentors like Gregg Pollack, Eric Berry, Nate Hopkins, Cliff Ravenscraft, David Brady, Dave Jackson, and many more.

Charles Max Wood explains how he landed his first 4 freelance clients that took him through a few years of freelancing with only 3 years of experience and a few hundred podcast listeners. Funnily enough, they actually came to him, not the other way around.

Chris explains how Tensorflow has grown over the last several years and the how it can be used to build and grow Machine Learning Systems. He explains the different algorithms you can use and the different types of problems it can solve.

Miguel Morales is a Machine Learning engineer at Lockheed Martin and teaches at Georgia Institute of Technology. This episode starts with a basic explanation of Reinforcement Learning. Miguel then talks through the various methods of implementing and training systems through Reinforcement Learning. We talk algorithms and models and much more…

John-Daniel Trask, founder and CEO of Raygun, talks about his experience building a monitoring company and about how to measure the speed and quality of your code.

John-Daniel Trask, founder and CEO of Raygun, talks about his experience building a monitoring company and about how to measure the speed and quality of your code.

Charles Max Wood takes a solo flight into how to make an impact on the development community and build the career you want at the same time. Chuck starts out summarizing his journey over the last year or so and then dives into his vision of how people can grow into becoming an influencer and using that to create opportunities in your life and career.

Peter Elger and Eóin Shanaghy join Charles Max Wood to dive into what Artificial Intelligence and Machine Learning related services are available for people to use. Peter and Eóin are experts in AWS and explain what is provided in its services, but easily extrapolate to other clouds. If you're trying to implement Artificial Intelligence algorithms, you may want to use or modify an algorithm already built and provided to you.

Jean-Georges Perrin compares Apache Spark to an operating system for data management. He explains how it can be used to pull data from disparate data sources, process the data, feed it into Machine Learning algorithms and stream the data out to other data streaming services.

Rishal Hurbans is the author of Grokking Artificial Intelligence Algorithms. He walks us through how to learn different Machine Learning algorithms. He also then walks us through the different types of algorithms based on different natural systems and processes.

Ken Youens-Clark regales us with his history through Jazz, Microsoft Tech, and Python and bioinformatics. Regular expressions are a fundamental part of data identification and cleaning. Also, we touch on the importance of types and tests as a specification for yourself and others. Python, as a data-science starting point, should be clean, functional, and friendly. Aspiring data scientists can learn a lot about the importance of fundamentals and clear encodings that fit the desired needs.

Alexey Grigorev is the lead data scientist for one of the biggest classified ads companies in the world. He walks us through gathering, mining, and understanding data to improve things. One big component of this is Machine Learning. It optimizes business processes and helps data scientists understand the data they have.

Mark Ryan is the lead Data Scientist for an insurance company in Toronto. He walks us through the ins and outs of structured data, how to manage it, and how to build Machine Learning systems. His book walks the reader through building a system that predicts whether bus routes in Toronto will be late using public domain data.

Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time. He has read hundreds of books that have taught him the secrets to getting more done by getting into this state.

In this episode of Adventures in Machine Learning, the amazing author and course creator Frank Kane entertains our panel with information and examples. Beril Sirmacek, Gant Laborde, Daniel Svoboda, & Charles Wood talk with Frank Kane about recommender systems.

Nick Chase is the author of the video series “Machine Learning for Mere Mortals.” He helps break down some of the misconceptions about how complicated Machine Learning is and the magical parts of the science. He and the panelists then dive into the basics of what you need to know and break up the scary sounding terms and mathematical concepts into bite size pieces.

In this episode of Adventures in Machine Learning, the panelists chat with Laurence Moroney about the history of AI in the UK. We talk about the AI overlords, ethics, and the need for teaching. Industry revolutions and how we can adapt them to improve life. Laurence is working on new synthetic datasets so tune in and check it out!

In this episode of Adventures in Machine Learning, Charles and Gant chat with Milecia about applying AI to UI/UX and the conversation takes a creative turn as they discuss plenty of other fun-filled and exciting topics she’s working on. There’s banter about self-driving cars and golf carts as they apply AI/ML to practical and non-practical uses.

In this week’s episode of Adventures in Machine Learning we have Hassan Kane, data scientist lead at Entropy Labs. Hassan discusses his journey from being raised in Ivory Coast, Africa to getting his education in computer science from MIT, to his discovery and embracing of machine learning. Hassan discusses various applications of machine learning, including that of NASA, satellites and edge devices..

TensorFlow is a machine learning library that allows a user to program deep learning architectures. It is normally associated with backend programming languages like Python and is written in C++, but what if you can utilize it in Javascript to program deep learning models for frontend web applications. Guest Jason Mayes talks about doing this with Tensorflow JS.

Beril Sirmacek is a data scientist and an assistant professor at Jonkoping University. She explains what computer vision is, what type of projects are done with it, and her own work regarding it.

Benson Ruan talks about his experiences as a machine learning tech lead for a fintech company in Sydney, Australia. He goes over his education in machine learning from Coursera, especially doing Andrew Ng’s deep learning course that started his journey up to his current position.

One of the hottest fields right now in machine learning is natural language processing. Whether it’s getting sentiment from tweets, summarizing your documents, sarcasm detection, or predicting stock trends from the news, NLP is definitely the wave of the future. Special guest Daniel Svoboda talks about transfer learning and the latest developments such as BERT that promises to revolutionize NLP even further.