4 minutes | Jul 8, 2018
She Thinks Machine Learnings Sexy
She Thinks Machine Learnings Sexy by machinelrn
45 minutes | Mar 7, 2018
Become a Machine Learning Expert in Under an Hour
Become a Machine Learning Expert in Under an Hour - Show Notes Special guest Domenic Puzio! Naturally you are excited about Machine Learning/ Deep Learning and you would love to join the party! After all, AI is going to revolutionize, in one way or another, almost everything we thought we knew about - everything! But what is the fastest way to enter the field of modern AI? Our guest, Domenic is getting ready to speak at SXSW for a talk is titled "How to Become a Machine Learning Expert in Under an Hour". It's a crash course in all things Machine Learning/ Deep Learning. This podcast is an 80/20 analysis to help new folks get up to speed on ML/DL fast, with no wasted effort or obsoleted information. The field is evolving so rapidly that yesterday's best practices are obsolete almost before the ink is dry. " As the AI revolution finds its value-creating ways into every part of our lives, we chat about rarely covered topics such as hype vs reality. " Where is AI technology headed next, thanks to cheap processing power and abundant online data streams and huge data corpii which are growing daily? " How can AI solve the data overload problems for business, government, defense, research and in our personal lives? Highlights: " Dominick's background as a hands-on ML practitioner. " Classic ML applications & examples o ML for Cybersecurity. o Types of datasets (North-South, East-West) and recommended moves for Cybersecurity applications using AI " Best ways and resources to get started now (Andrew N? Ian G's book? Or jumping feet first with a Hello World). " What is the "Golden Certification" a groundbreaking new credential to demonstrate you know AI end-to-end. " What ML/DL framework is the best for getting started? o Should I start out on a high-end gamer laptop with Nvidia GPU? (Hint: NO!) " Math. What do I need to know now? (Hint: just-in-time learning and the important role of intuitive understanding vs formalism in AI math) " Demystifying things. What is going on under the hood of neural networks. Are we close to Skynet? " How does this scale to a large global company or agency with legacy infrastructure? And how to a show AI value quickly. " How do we implement enterprise Machine Learning/ Deep Learning behind the firewall in a security conscious environment? How do we securely train models and what are some best practices? Links: Become a Deep Learning Coder from Scratch in Under a Year https://lifehacker.com/become-a-deep-learning-coder-from-scratch-in-under-a-ye-1822763353 How to learn Deep Learning in 6 months https://towardsdatascience.com/how-to-learn-deep-learning-in-6-months-e45e40ef7d48 How do I learn deep learning in 2 months? https://www.quora.com/How-do-I-learn-deep-learning-in-2-months Become a Machine Learning Expert in Under an Hour https://medium.com/capitalonetech/become-a-machine-learning-expert-in-under-an-hour-8437939ae1e2 Solutions for government and defense contractors http://federal.ai Solutions for mid and large cap commercial customers http://machinelrn.com Social Media and Contact Info: Domenic's LinkedIn profile https://www.linkedin.com/in/domenicpuzio/ Domenic on Twitter https://twitter.com/MachineLearnMe MachineLrn and MachineLrnRadio on Twitter https://twitter.com/Machine_lrn and https://twitter.com/MachineLrnRadio Lloyd's "TensorPro" study group on Facebook https://www.facebook.com/groups/1749618838685711/
5 minutes | Feb 5, 2018
MR AI - Artificial Intelligence meets gangster rap
AI is the next industrial revolution, poised to rapidly reinvent business, the global economy and… gangster rap. Enjoy!
6 minutes | Dec 21, 2017
Machine Learning For Kids
Joined today by the world’s youngest machine learning engineer! She was inspired by “Age of Ultron” But how does machine learning actually work? We followed up this podcast with the Teachable Machine project based on a new library called deeplearn.js, which makes it easier for any web dev to get into machine learning. ML relies on specific representation of data, a set of features that are understandable for a computer. If we’re talking about text it should be represented through the words it contains or some other characteristics such as length of the text etc. All ML tasks can be classified in several categories, the main ones are: • Supervised ML • Unsupervised ML • Reinforcement learning. Supervised ML relies on data where the true label/class was indicated. This is easier to explain using an example. Let us imagine that we want to teach a computer to distinguish pictures of cats and dogs. We can ask some of our friends to send us pictures of cats and dogs adding a tag Cat or Dog. Follow-up questions: • Why did they want better machines? • How do you imagine and build something that doesn’t exist yet?
7 minutes | Dec 15, 2017
Episode 3 AI Never Sleeps! – Real-world business discussion about QRC AI deployments
Throughout these podcasts, we will focus on hands-on implementation of AI, deep learning, and machine learning with regard to (wrt) the actual engineering of ML solutions into real world enterprises and networks. Wednesday I had the pleasure of meeting with a group of DC area C-level execs who are looking for some deep learning classifiers to add to their product lines. I have had some minor dealings with them in the past, so I know these folks are the real deal among senior executives who are both AI-savvy and know how to handle disruptive technology transitions for large enterprises. The CTO is sharp as a whip (Genius IQ, thinks out of the box, and knows BS and fluff when he hears it). Like many decision makers whose time is critical, he employs a very abrasive delivery as a way to cut to the chase. Meeting basically went like this: (CTO:) Lloyd thanks for fighting DC traffic, welcome. So why MachineLrn and not the PhD body shops we know and love? Those guys have been working on AI for 20 years and have a large staff of PhD and PhD candidate students. My Answer: No debating, they are a big shop of smart AI people. However, they have been heads down working on their own custom frameworks, hand coded predictive models, custom analytics/ analytic engines, and white papers tons and tons of whitepapers. (And usually training on made-up data corpus and cherry picked labelled data). Based on my systems engineering experience, the most frequent question I get from these AI SMEs is “where can we get a representative corpus of data which reflects the real world within your enterprise?” Essentially, I recommend leveraging the world’s largest internet company and the authority in artificial intelligence. Those other guys will fly in some PhDs and spend tons of billable time tweaking their inferior product. The end result is to slow roll AI deployment, wander down blind alleys are your billable expense, and ultimately play catchup with commercial AI developments. BL: would you like to start transforming your company with QRC AI deployments, or to kick off an open ended study and pilots which make great lab demos but fail to smoothly integrate? Why put this out there? This is real world! I didn’t plan this episode! I didn’t expect a call two days ago for a DC meetup. I didn’t expect such a polarizing question. Such is the urgency and angst among the CTOs that I should have expected it. They know from past sad experiences what doesn’t work, but they also appreciate that AI is here now. If you read my article on “Why AI will revolutionize computer programming”, it will be crystal clear that AI will completely transform the old concepts of MMI, while also create new knowledge by deep learning from enormous data carpi far too large for human processing. If I can provide my own lessons- learned to someone else who can push through and get traction it was totally worth it. If you have better ideas (and you probably will if you read this far), please share them here. I’m not anti-academic. Out of decades of brilliant fundamental research into adaptive filtering, later sigmoid and back prop for generic problem solving algorithms the researchers have delivered data science tools, algorithms and computer science breakthoughs. These developments emerge just in time to benefit from the confluence of data center cheap processing, specialize GPU/TPU processing, and mass (global) data storage. Also, now is now the right time to start an AI QRC pilot, leveraging the enormous investment of current COTS AI technology. Do not wait for a whitepaper or your competition will get a head start.
9 minutes | Dec 12, 2017
Episode 2 AI and Machine Learning Terminology
Welcome to the MachineLrn Podcast. We are a leading online resource for hands-on implementation of AI, deep learning, and machine learning. There are lots of theory-oriented web resources for this exploding field, but there is very little information on the actual engineering of ML solutions into real world enterprises and networks. This is a short episode and is “technically” episode #2 in the MachineLrn Podcast series. The first episode set the proper mood with the first rap song (in history) written about AI. It was entitled “AI State of Mind” and you can listen to it here: https://soundcloud.com/machinelrn/ai-state- of-mind Today we will keep it short. Powerful as it is, ML is chockablock full of technical jargon, algorithms and totally non-intuitive terminology. Collectively, this jumble of confusing terms and abbreviations create a tremendous barrier for newbies. Cynics may argue this was done on purpose, a way to hide simple concepts from newcomers and customers alike But the reality is that ML is a field with very deep historical roots across the math, computer science, and data research fields. The result is an enormous corpus of historical AI terminology which is at once beautiful, awesome, (and for most) incomprehensible to behold. Regardless of your role (manager, engineer, student, CEO) you should become familiar with the terminology of AI, the concepts and the most frequently used terms and abbreivations. To kick this journey off on the right foot, here are “Lloyd’s dirty dozen” - basic ML terms everyone should understand, regardless of background or position:
2 minutes | Dec 9, 2017
AI State of Mind
This is the world’s first rap song about artificial intelligence, deep learning, and machine learning. The idea that software can simulate the neocortex’s large array of neurons in an artificial neural network—isn’t new, and it has led to as many disappointments as breakthroughs. Today, because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before. We captured these powerful new breakthroughs in a rap song that includes shout-outs to market leaders and machine learning/ deep learning frameworks. Enjoy!