Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2

Daniel Bourke · Beginner ·📐 ML Fundamentals ·5y ago

Key Takeaways

This video introduces the fundamentals of TensorFlow and deep learning with Python through a code-first introduction

Original Description

Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place. After this two-part code-first introduction, you’ll have written 100s of lines of TensorFlow code and have hands-on experience with two important problems in machine learning: regression (predicting a number) and classification (predicting if something is one thing or another). Open a Google Colab (if you’re not sure what this is, you’ll find out soon) window and get ready to code along. Sign up for the full course - https://dbourke.link/ZTMTFcourse Get all of the code/materials on GitHub - https://www.github.com/mrdbourke/tensorflow-deep-learning/ Ask a question - https://github.com/mrdbourke/tensorflow-deep-learning/discussions See part 2 - https://youtu.be/ZUKz4125WNI TensorFlow Python documentation - https://www.tensorflow.org/api_docs/python/tf Connect elsewhere: Web - https://www.mrdbourke.com Livestreams on Twitch - https://www.twitch.tv/mrdbourke Get email updates on my work - https://www.mrdbourke.com/newsletter Timestamps: 0:00 - Intro/hello/how to approach this video 1:50 - MODULE 0 START (TensorFlow/deep learning fundamentals) 1:53 - [Keynote] 1. What is deep learning? 6:31 - [Keynote] 2. Why use deep learning? 16:10 - [Keynote] 3. What are neural networks? 26:33 - [Keynote] 4. What is deep learning actually used for? 35:10 - [Keynote] 5. What is and why use TensorFlow? 43:05 - [Keynote] 6. What is a tensor? 46:40 - [Keynote] 7. What we're going to cover 51:12 - [Keynote] 8. How to approach this course 56:45 - 9. Creating our first tensors with TensorFlow 1:15:32 - 10. Creating tensors with tf Variable 1:22:40 - 11. Creating random tensors 1:32:20 - 12. Shuffling the order of tensors 1:42:00 - 13. Creating tensors from NumPy arrays 1:53:57 - 14. Getting information from our tensors 2:05:52 - 15. Indexing and expanding tensors 2:18:27 - 16. Manipulating tensors with basic operations 2:24:00 - 17. Matrix multiplication part 1 2:35:55
Watch on YouTube ↗ (saves to browser)
AI explanation not available for this lesson yet
This lesson is still being prepared for the AI tutor. In the meantime, explore lessons that are ready.
Browse explainer-ready lessons →

Playlist

Uploads from Daniel Bourke · Daniel Bourke · 0 of 60

← Previous Next →
1 Xbox One S Unboxing and Xbox One Comparison
Xbox One S Unboxing and Xbox One Comparison
Daniel Bourke
 2 Text/Profanity Checker in Python
Text/Profanity Checker in Python
Daniel Bourke
 3 Drawing Flowers in Python
Drawing Flowers in Python
Daniel Bourke
 4 Finding The Right Medium - TDBS 18 April 2017
Finding The Right Medium - TDBS 18 April 2017
Daniel Bourke
 5 What Is Neuralink??! - TDBS 22 April 2017
What Is Neuralink??! - TDBS 22 April 2017
Daniel Bourke
 6 Disagree and Commit, Words of Wisdom from Jeff Bezos - TDBS 19 April 2017
Disagree and Commit, Words of Wisdom from Jeff Bezos - TDBS 19 April 2017
Daniel Bourke
 7 A Lesson In Movement | Raw Training Australia
A Lesson In Movement | Raw Training Australia
Daniel Bourke
 8 FALLING IS FUN | Functional Friday 4
FALLING IS FUN | Functional Friday 4
Daniel Bourke
 9 My first HACKATHON! | 100 Days of Code 1
My first HACKATHON! | 100 Days of Code 1
Daniel Bourke
 10 MORE MACHINE LEARNING | 100 Days of Code 2
MORE MACHINE LEARNING | 100 Days of Code 2
Daniel Bourke
 11 TensorBoard and learning from Einstein | 100 Days of Code 3
TensorBoard and learning from Einstein | 100 Days of Code 3
Daniel Bourke
 12 Job Interview Tips and Open Ocean Swim | 100 Days of Code 4
Job Interview Tips and Open Ocean Swim | 100 Days of Code 4
Daniel Bourke
 13 I Want To Help 100,000 People Workout | AI Powered Personal Trainer
I Want To Help 100,000 People Workout | AI Powered Personal Trainer
Daniel Bourke
 14 MACHINE LEARNING IN 5 MINUTES
MACHINE LEARNING IN 5 MINUTES
Daniel Bourke
 15 COFFEE, YOGA and AWS | 100 Days of Code 5
COFFEE, YOGA and AWS | 100 Days of Code 5
Daniel Bourke
 16 MY FIRST STARTUP WEEKEND | 100 Days of Code 6
MY FIRST STARTUP WEEKEND | 100 Days of Code 6
Daniel Bourke
 17 GENERATING TV SCRIPTS WITH DEEP LEARNING | 100 Days of Code 7
GENERATING TV SCRIPTS WITH DEEP LEARNING | 100 Days of Code 7
Daniel Bourke
 18 Attention, please
Attention, please
Daniel Bourke
 19 TEACHING BOTS TO PLAY GAMES | 100 Days of Code 9
TEACHING BOTS TO PLAY GAMES | 100 Days of Code 9
Daniel Bourke
 20 Udacity Deep Learning Nanodegree Language Translation Project Submission | 100 Days of Code 10
Udacity Deep Learning Nanodegree Language Translation Project Submission | 100 Days of Code 10
Daniel Bourke
 21 Learning about Generative Adversarial Networks on Udacity | 100 Days of Code 11
Learning about Generative Adversarial Networks on Udacity | 100 Days of Code 11
Daniel Bourke
 22 Completing Andrew Ng's Machine Learning Course on Coursera | 100 Days of Code 12
Completing Andrew Ng's Machine Learning Course on Coursera | 100 Days of Code 12
Daniel Bourke
 23 Finishing the Treehouse Python Track | 100 Days of Code 13
Finishing the Treehouse Python Track | 100 Days of Code 13
Daniel Bourke
 24 GENERATING FACES WITH GANs | 100 Days of Code 14
GENERATING FACES WITH GANs | 100 Days of Code 14
Daniel Bourke
 25 Graduating From the Udacity Deep Learning Nanodegree | 100 Days of Code 15
Graduating From the Udacity Deep Learning Nanodegree | 100 Days of Code 15
Daniel Bourke
 26 WHAT I'VE LEARNED FROM TALKING TO PEOPLE
WHAT I'VE LEARNED FROM TALKING TO PEOPLE
Daniel Bourke
 27 3 Life Principles I Learned From Ray Dalio
3 Life Principles I Learned From Ray Dalio
Daniel Bourke
 28 PYTHON && POETRY | 100 Days of Code 16
PYTHON && POETRY | 100 Days of Code 16
Daniel Bourke
 29 Physique Update and 6 Things I Wish I Knew Before Starting Gym
Physique Update and 6 Things I Wish I Knew Before Starting Gym
Daniel Bourke
 30 The 100 Days is Over! | 100 Days of Code 17
The 100 Days is Over! | 100 Days of Code 17
Daniel Bourke
 31 How to Burn Over 100 Calories in 4 Minutes
How to Burn Over 100 Calories in 4 Minutes
Daniel Bourke
 32 Solving Sudoku with AI | Learning Intelligence 1
Solving Sudoku with AI | Learning Intelligence 1
Daniel Bourke
 33 Upper Body Calisthenics Workout in the Park
Upper Body Calisthenics Workout in the Park
Daniel Bourke
 34 What is an Adversarial Search Agent? | Learning Intelligence 2
What is an Adversarial Search Agent? | Learning Intelligence 2
Daniel Bourke
 35 My Self-Created Artificial Intelligence Master's Degree | Learning Intelligence 0
My Self-Created Artificial Intelligence Master's Degree | Learning Intelligence 0
Daniel Bourke
 36 Try Going Over It Again | Learning Intelligence 3
Try Going Over It Again | Learning Intelligence 3
Daniel Bourke
 37 Python and Pullups | Learning Intelligence 4
Python and Pullups | Learning Intelligence 4
Daniel Bourke
 38 AI Meets Blockchain! | Learning Intelligence 5
AI Meets Blockchain! | Learning Intelligence 5
Daniel Bourke
 39 How to Pass the Turing Test + I FAILED | Learning Intelligence 6
How to Pass the Turing Test + I FAILED | Learning Intelligence 6
Daniel Bourke
 40 Biology and Physics meet Computer Science | Learning Intelligence 7
Biology and Physics meet Computer Science | Learning Intelligence 7
Daniel Bourke
 41 Udacity Artificial Intelligence Nanodegree Project 3 Progress | Learning Intelligence 8
Udacity Artificial Intelligence Nanodegree Project 3 Progress | Learning Intelligence 8
Daniel Bourke
 42 Passing Project 3 of Udacity's Artificial Intelligence Nanodegree | Learning Intelligence 9
Passing Project 3 of Udacity's Artificial Intelligence Nanodegree | Learning Intelligence 9
Daniel Bourke
 43 Bayes Networks, Hidden Markov Models and How I Wake Up | Learning Intelligence 10
Bayes Networks, Hidden Markov Models and How I Wake Up | Learning Intelligence 10
Daniel Bourke
 44 Udacity AI Nanodegree Progress and Bayes' Rule Explained | Learning Intelligence 11
Udacity AI Nanodegree Progress and Bayes' Rule Explained | Learning Intelligence 11
Daniel Bourke
 45 Udacity AI Nanodegree Project 4 Planning and Progress | Learning Intelligence 12
Udacity AI Nanodegree Project 4 Planning and Progress | Learning Intelligence 12
Daniel Bourke
 46 Finishing Term 1 of Udacity's Artificial Intelligence Nanodegree | Learning Intelligence 13
Finishing Term 1 of Udacity's Artificial Intelligence Nanodegree | Learning Intelligence 13
Daniel Bourke
 47 deeplearning.ai Progress! | Learning Intelligence 14
deeplearning.ai Progress! | Learning Intelligence 14
Daniel Bourke
 48 Coursera Deep Learning Specialization Progress | Learning Intelligence 15
Coursera Deep Learning Specialization Progress | Learning Intelligence 15
Daniel Bourke
 49 Computer Vision Basics + More deeplearning.ai Progress! | Learning Intelligence 16
Computer Vision Basics + More deeplearning.ai Progress! | Learning Intelligence 16
Daniel Bourke
 50 My Experience at CodeCamp, Intro to Keras and Failing Hard | Learning Intelligence 17
My Experience at CodeCamp, Intro to Keras and Failing Hard | Learning Intelligence 17
Daniel Bourke
 51 In-Depth Udacity Deep Learning Nanodegree Review
In-Depth Udacity Deep Learning Nanodegree Review
Daniel Bourke
 52 Completing the Deeplearning.ai Specialization on Coursera | Learning Intelligence 18
Completing the Deeplearning.ai Specialization on Coursera | Learning Intelligence 18
Daniel Bourke
 53 You're Never Too Young to Start Learning AI - Learning Intelligence Talks with Shaik Asad
You're Never Too Young to Start Learning AI - Learning Intelligence Talks with Shaik Asad
Daniel Bourke
 54 Starting Term 2 of the Udacity Artificial Intelligence Nanodegree | Learning Intelligence 19
Starting Term 2 of the Udacity Artificial Intelligence Nanodegree | Learning Intelligence 19
Daniel Bourke
 55 Submitting the Computer Vision Capstone Project | Udacity AI Nanodegree | Learning Intelligence 20
Submitting the Computer Vision Capstone Project | Udacity AI Nanodegree | Learning Intelligence 20
Daniel Bourke
 56 Leg Day at World Gym Northlakes ft. Ben Jones Fitness
Leg Day at World Gym Northlakes ft. Ben Jones Fitness
Daniel Bourke
 57 deeplearning.ai Sequence Models Course Progress | Learning Intelligence 21
deeplearning.ai Sequence Models Course Progress | Learning Intelligence 21
Daniel Bourke
 58 Graduating from the deeplearning.ai Coursera Specialization | Learning Intelligence 22
Graduating from the deeplearning.ai Coursera Specialization | Learning Intelligence 22
Daniel Bourke
 59 Udacity Artificial Intelligence Nanodegree NLP Concentration Progress | Learning Intelligence 23
Udacity Artificial Intelligence Nanodegree NLP Concentration Progress | Learning Intelligence 23
Daniel Bourke
 60 Learning How to Build What's Next at Google Cloud On Board Brisbane
Learning How to Build What's Next at Google Cloud On Board Brisbane
Daniel Bourke

Related Reads

📰
From Rule-Based AI to Generative AI: How Artificial Intelligence Learned to Plan, Decide, and…
Learn how AI evolved from rule-based systems to generative AI, enabling machines to plan, decide, and act, and understand the key concepts and techniques involved
Medium · Machine Learning
 📰
How I Reduced Sudoku Backtracking by 98.99% Using Set Theory and Python
Learn how to optimize Sudoku solving using set theory and Python, reducing backtracking by 98.99%
Dev.to AI
 📰
Predicting Customer Happiness with Machine Learning: Working with Small Datasets
Learn to predict customer happiness using machine learning with small datasets by leveraging nested cross-validation, feature reduction, and threshold tuning
Medium · Machine Learning
 📰
No Free Lunch Theorem: A Machine Learning Concept You Should Know
Learn the No Free Lunch Theorem, a fundamental concept in Machine Learning that explains why no single algorithm can outperform all others on every problem, and why it matters for model selection and optimization.
Medium · Data Science

Chapters (19)

Intro/hello/how to approach this video
1:50 MODULE 0 START (TensorFlow/deep learning fundamentals)
1:53 [Keynote] 1. What is deep learning?
6:31 [Keynote] 2. Why use deep learning?
16:10 [Keynote] 3. What are neural networks?
26:33 [Keynote] 4. What is deep learning actually used for?
35:10 [Keynote] 5. What is and why use TensorFlow?
43:05 [Keynote] 6. What is a tensor?
46:40 [Keynote] 7. What we're going to cover
51:12 [Keynote] 8. How to approach this course
56:45 9. Creating our first tensors with TensorFlow
1:15:32 10. Creating tensors with tf Variable
1:22:40 11. Creating random tensors
1:32:20 12. Shuffling the order of tensors
1:42:00 13. Creating tensors from NumPy arrays
1:53:57 14. Getting information from our tensors
2:05:52 15. Indexing and expanding tensors
2:18:27 16. Manipulating tensors with basic operations
2:24:00 17. Matrix multiplication part 1
Up next
How Netflix Handles Large Imbalanced Datasets | Machine Learning Case Study Explained
Pavithra’s Podcast
Watch →