When I teach Deep Learning, the question I get most often is: "Should I be using TensorFlow or PyTorch?" In this recent talk at the DataScienceGO conference, I provide my most thorough and polished response yet.
Thanks to Harpreet Sahota for hosting the session masterfully and leading the audience Q&A at the end.
Filtering by Tag: tensorflow
From Data Science to Cinema
SuperDataScience SuperStar Hadelin returns to report on his journey from multi-million-selling video instructor to mainstream-film actor — and he details the traits that allow data scientists to succeed at anything.
Hadelin has created and presented 30 extremely popular Udemy courses on machine learning topics, selling over two million copies so far. Prior to his epic creative period publishing ML courses, Hadelin studied math, engineering and A.I. at the Université Paris-Saclay and he worked as a data engineer at Google. More recently Hadelin has written a book called "A.I. Crash Course" and was co-founder and CEO of BlueLife AI.
Today's episode focuses on:
• Hadelin's recent shift toward acting in mainstream films
• The characteristics that enable an outstanding data scientist to excel in any pursuit
• How to cultivate your passion and achieve your dreams
• Bollywood vs Hollywood
• How to prepare for the TensorFlow Certificate Program
• Software modules for deploying deep learning models into production
Listen or watch here.
AutoDiff with TensorFlow
PyTorch and TensorFlow are by far the two most widely-used automatic-differentiation libraries. Last week, we used PyTorch to differentiate an equation automatically and instantaneously. Today, we do it with TensorFlow.
(For an overview of the pros and cons of PyTorch versus TensorFlow, I've got a talk here. The TLDR is you should know both!)
A new video for my "Calculus for ML" course published on YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Automatic Differentiation – Segment 3 of Subject 3, "Limits & Derivatives" – Machine Learning Foundations
Automatic Differentiation is a computational technique that allows us to move beyond calculating derivatives by hand and scale up the calculation of derivatives to the massive scales that are common in machine learning.
The YouTube videos in this segment, which we'll release every Wednesday, introduce AutoDiff in the two most important Python AutoDiff libraries: PyTorch and TensorFlow.
My growing "Calculus for ML" course is available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
DataScienceGo This Weekend
The DataScienceGO conference is this weekend — registration for Friday and Saturday is 100% free! I'm speaking Saturday on the pros and cons of TensorFlow vs PyTorch for training and deploying deep-learning models.
Awesome speakers — whom you may already be familiar with from recent SuperDataScience episodes — include:
• Erica Greene (episode # 435)
• Harpreet Sahota (# 457)
• Andrew Jones (# 483)
I don't (yet!) personally know the other speakers pictured here but their weighty reputations precede them and I'm looking forward to getting to know them better over the course of the weekend: Gabriela de Queiroz, Karen JEAN-FRANCOIS, Yudan Lin, Ken Jee, and Danny Ma.
Free registration here!
TensorFlow vs PyTorch @ DataScienceGo Virtual
The DataScienceGO Virtual conference is coming up next Saturday and it is FREE! I'm giving a talk on TensorFlow vs PyTorch with lots of time for audience questions.
Upcoming O'Reilly Calculus Classes
Starting a week today, I'm offering my entire "ML Foundations" curriculum as a series of 14 live, interactive workshops via O'Reilly Media. The first five classes are open for registration; two are already waitlist-only, so grab a spot now:
• Jul 14 — Intro to Linear Algebra (waitlisted)
• Jul 21 — LinAlg II: Matrix Tensors (5 spots remaining)
• Jul 28 — LinAlg III: Eigenvectors (waitlisted)
• Aug 12 — Intro to Calculus (143 spots remaining)
• Aug 18 — Calc II: AutoDiff (148 spots remaining)
REGARDING THE WAITLIST: I have a made a request with O'Reilly to increase the maximum class size from 600 students to 1000, so if you sign up for a waitlisted class now, you should still be able to get in.
Overall, there will be four subject areas covered:
• Linear Algebra (3 classes)
• Calculus (4 classes)
• Probability and Statistics (4 classes)
• Computer Science (3 classes)
Sign up opens about two months prior to each class. All 14 training dates, running from next week through December, are provided at jonkrohn.com/talks
A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.