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This is a clip from a conversation with Bjarne Stroustrup from Nov 2019. New full episodes are released once or twice a week and 1-2 new clips or a new non-podcast video is released on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=uTxRF5ag27A
(more links below)
Podcast full episodes playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO
Podcasts clips playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO
Podcast website:
https://lexfridman.com/ai
Podcast on Apple Podcasts (iTunes):
https://apple.co/2lwqZIr
Podcast on Spotify:
https://spoti.fi/2nEwCF8
Podcast RSS:
https://lexfridman.com/category/ai/feed/
Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow. So for a little while, I'll keep posting clips on this channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting.
Bjarne Stroustrup is the creator of C++, a programming language that after 34 years is still one of the most popular and powerful languages in the world. Its focus on fast, stable, robust code underlies many of the biggest systems in the world that we have come to rely on as a society. If you're watching this on YouTube, many of the critical back-end component of YouTube are written in C++. Same goes for Google, Facebook, Amazon, Twitter, most Microsoft applications, Adobe applications, most database systems, and most physical systems that operate in the real-world like cars, robots, rockets that launch us into space and one day will land us on Mars.
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Ready to explore machine learning and artificial intelligence in python? This python machine learning and AI mega course contains 4 different series designed to teach you the ins and outs of ML and AI. It talks about fundamental ML algorithms, neural networks, creating AI chat bots and finally developing an AI that can play the game of Flappy Bird.
⭐️ Thanks to Kite for sponsoring this video! Download the best AI automcolplete for python programming for free: https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=python-ml-ai-mega-course
⭐ RESOURCES ⭐
IMPORTANT: The text-based guides will have download links for files or datasets needed.
1⃣ Machine Learning for Beginners
💻 Text-Based Guide: https://techwithtim.net/tutori....als/machine-learning
💾 UCI Student Data Set: https://archive.ics.uci.edu/ml..../datasets/Student+Pe
💾 UCI Car Evaluation Data Set: http://techwithtim.net/wp-cont....ent/uploads/2019/01/
2⃣ Neural Networks
💻 Text-Based Guide: https://techwithtim.net/tutori....als/python-neural-ne
3⃣ Simple AI Chat Bot
💻 Text-Based Guide: https://techwithtim.net/tutorials/ai-chatbot/
💾 JSON-File Download: https://techwithtim.net/wp-con....tent/uploads/2019/05
4⃣ Flappy Bird AI
💻 GitHub/Code: https://github.com/techwithtim/NEAT-Flappy-Bird
💾 Images: https://techwithtim.net/wp-con....tent/uploads/2019/08
⭐ SOFTWARE DOWNLOADS ⭐
🔗 Anaconda Download: https://www.anaconda.com/download/
🔗 Pycharm Download: https://www.jetbrains.com/pych....arm/download/#sectio
⭐ TIMESTAMPS ⭐
🎥 Course 1: Machine Learning for Beginners 🎥
⌨️ (0:00) Course Introduction
⌨️ (00:02:30) Introduction to Machine Learning & Environment Setup
⌨️ (00:12:24) Linear Regression Part 1 – Data Loading and Analysis
⌨️ (00:26:28) Linear Regression Part 2 – Implementation and Algorithm Explanation
⌨️ (00:42:50) Saving Models and Visualizing Data
⌨️(00:56:05) K-Nearest Neighbors Part 1 – Irregular Data
⌨️ (01:08:16) K-Nearest Neighbors Part 2 – Algorithm Explanation
⌨️ (01:21:33) K-Nearest Neighbors Part 3 – Implementation
⌨️ (01:31:54) Support Vector Machines Part 1 - SkLearn Datasets and Analysis
⌨️ (01:38:34) Support Vector Machines Part 2 – Algorithm Explanation
⌨️ (01:52:21) Support Vector Machines Part 3 – Implementation
⌨️(02:01:57) K-Means Clustering – Algorithm Explanation
⌨️ (02:15:11) K-Means Clustering - Implementation
🎥 Course 2: Neural Networks 🎥
⌨️ (02:27:07) Introduction to Neural Networks
⌨️ (02:53:47) Loading & Looking at Data
⌨️ (03:06:50) Creating a Model
⌨️ (03:24:05) Using and Testing Our Model
⌨️ (03:33:56) Text Classification Part 1 – Data Analysis and Model Architecture
⌨️ (03:55:23) Text Classification Part 2 – Embedding Layers
⌨️ (04:09:43) Text Classification Part 3 – Training the Model
⌨️ (04:19:49) Text Classification Part 4 – Saving and Loading Models
🎥 Course 3: AI Chat Bot 🎥
⌨️ (04:34:35) Part 1
⌨️ (04:50:28) Part 2
⌨️ (05:02:39) Part 3
⌨️ (05:14:32) Part 4
⌨️ (05:30:34) Part 5
🎥 Course 4: Neuroevolutionary Algorithm Plays Flappy Bird 🎥
⌨️ (05:39:16) Creating the Bird
⌨️ (05:51:36) Moving the Bird
⌨️ (06:10:04) Pixel Perfect Collision
⌨️ (06:29:22) Finishing the Graphics
⌨️ (06:41:16) NEAT Introduction and Configuration File
⌨️ (07:01:36) Implementing NEAT and Fitness Functions
⌨️ (07:16:32) Testing and Saving Models
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💻 Enroll in The Fundamentals of Programming w/ Python
https://tech-with-tim.teachable.com/p...
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Tags:
- Tech With Tim
- Python Tutorials
- Machine Learning Course
- AI Course Python
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#Python #MachineLearning #AI
It seems like more and more applications and machines are getting on the image recognition train. It's a cool feature to have because it can assist society in many ways, such as boosting productivity and safety if we're dealing with automated vehicles.
Today on Feed My Curiosity, we look at how image recognition works. How does a machine learn what something looks like? Watch to find out!
Our Networks:
Website - https://www.feedmycuriosity.net/
Reddit - https://www.reddit.com/r/FeedMyCuriosity/
Article - https://www.feedmycuriosity.net/2021/06/26/deep-learning-ai-how-image-recognition-works/
This is my solution to lrCostFunction.m function in Programming assignment 3 from the famous Machine Learning course by Andrew Ng.
Github: https://github.com/AladdinPerz....on/Courses/tree/mast
When talking about artificial intelligence (AI), deep learning and machine learning are invariably mentioned in the same breath. But what do they mean? Are they same thing? Are they different? In this video, we explain what machine learning and deep learning mean, and how they fit into the AI family.
Did you know? you can start building A.I. applications for free on IBM Cloud
N.B. this IBM Cloud hyperlink should link to https://ibm.biz/whatisvideo
If you want to learn more, we've prepared loads of great resources to help you like:
- Tutorials
- Code Patterns
- Datasets
- Models
You can find these and more at http://ibm.biz/Bdqyv6
Build Smart. Build secure. Join a global community of developers at http://ibm.biz/IBMdeveloperYT
#IBMWhatIs
#AI
#MachineLearning
#DeepLearning
#Learn