freeCodeCamp.org
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.
Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.
Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.
⭐️ Google Colaboratory Notebooks ⭐️
📕 Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true
📗 Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true
📘 Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true
📙 Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true
📔 Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true
📒 Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true
⭐️ Course Contents ⭐️
⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
⌨️ Module 2: Introduction to TensorFlow (00:30:08)
⌨️ Module 3: Core Learning Algorithms (01:00:00)
⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
⌨️ Module 8: Conclusion and Next Steps (06:48:24)
⭐️ About the Author ⭐️
The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below:
🔗 YouTube: https://www.youtube.com/channel/UC4JX40jDee_tINbkjycV4Sg
🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/
—
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Hii i am facing the following error …. "Input0 is incompatible with layer flattern_12
Nice explination ..!
Thanks a lot for this course
for module 3 I am getting {'accuracy': 0.625, 'accuracy_baseline': 0.625, 'auc': 0.5} everytime what I have done wrong here can anyone explain please help!
2:17:25 ahahahah whats that sound
why is there no documentation about the video
30:04 mark
Thank you very very very very much
You are applying a linear algorithm to predict a discrete output survived / not survived , how is that possible?
i love you
He just missed the chance to end the introduction perfectly at 30:00
Great Video Dude!! I just wished u could spend more time at CNNs
48:40 Bookmark
Good course but my 32 bit system not allowed tensorflow
I have gone through complete lecture and i have no idea. I seek to come here after 6 months and write down list of projects i did.I am starting from scratch. I am mechanical engineer.
Would you add "English Subtitle" for those who have a hard time understanding?
⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
⌨️ Module 2: Introduction to TensorFlow (00:30:08)
⌨️ Module 3: Core Learning Algorithms (01:00:00)
⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
⌨️ Module 8: Conclusion and Next Steps (06:48:24)
30:00
2:08:44 You say 0.39 is pretty bad:
me looks at my loss: sees 0.948
Great course 👌👌
why is module 2 in the description link different from the one shown?
Miles Dyson(Terminator 2) is watching this
Tim got 80% accuracy on the flowers classification while I am getting 95% with the same code. Same number of steps and same GNNClassifier. Is someone getting the same thing?
2:30:11