Building Neural Networks from Scratch Lecture 12 - Backpropagation from scratch on a single neuron
Lecture 12 - Backpropagation from scratch on a single neuron Transcript and Lesson Notes
In this lecture, we understand backpropagation from scratch. We implement backpropagation for a single neuron: first through writing on a whiteboard, and then through code. Google Colab Notebook for Forward Pass: https:/
Quick Summary
In this lecture, we understand backpropagation from scratch. We implement backpropagation for a single neuron: first through writing on a whiteboard, and then through code. Google Colab Notebook for Forward Pass: https:/
Key Takeaways
- Review the core idea: In this lecture, we understand backpropagation from scratch. We implement backpropagation for a single neuron: first through writing on a whiteboard, and then through code. Google Colab Notebook for Forward Pass: https:/
- Understand how lecture fits into Lecture 12 - Backpropagation from scratch on a single neuron.
- Understand how backpropagation fits into Lecture 12 - Backpropagation from scratch on a single neuron.
- Understand how from fits into Lecture 12 - Backpropagation from scratch on a single neuron.
- Understand how scratch fits into Lecture 12 - Backpropagation from scratch on a single neuron.
Key Concepts
Full Transcript
In this lecture, we understand backpropagation from scratch. We implement backpropagation for a single neuron: first through writing on a whiteboard, and then through code. Google Colab Notebook for Forward Pass: https://drive.google.com/file/d/1D9mPdUhW8N5pd6F5xlo2nAGwBQzfY6ip/view?usp=sharing Single Neuron Backpropagation Python code: https://drive.google.com/file/d/1303Htf85EnktcjqZjmfDLaBun0hj1F4s/view?usp=sharing ================================================= ✉️ Join our FREE Newsletter: https://vizuara.ai/our-newsletter/ ================================================= As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there. Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch. No cost. No hidden charges. Pure old school teaching and learning. ================================================= 🌟 Meet Our Team: 🌟 🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper) 🔗 LinkedIn: https://www.linkedin.com/in/raj-abhijit-dandekar-67a33118a/ 🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/rajat-dandekar-901324b1/ 🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/sreedath-panat-8a03b69a/ 🎓 Sahil Pocker (Machine Learning Engineer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/sahil-p-a7a30a8b/ 🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23) 🔗 LinkedIn: https://www.linkedin.com/in/abhijeet-singh-9a1881192/ 🎓 Sourav Jana (Software Developer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/souravjana131/
Lesson FAQs
What is Lecture 12 - Backpropagation from scratch on a single neuron about?
In this lecture, we understand backpropagation from scratch. We implement backpropagation for a single neuron: first through writing on a whiteboard, and then through code. Google Colab Notebook for Forward Pass: https:/
What key concepts are covered in this lesson?
The lesson covers lecture, backpropagation, from, scratch, single.
What should I learn before Lecture 12 - Backpropagation from scratch on a single neuron?
Review the previous lessons in Building Neural Networks from Scratch, then use the transcript and key concepts on this page to fill any gaps.
How can I practice after this lesson?
Practice by applying the main concepts: lecture, backpropagation, from, scratch.
Does this lesson include a transcript?
Yes. The full transcript is visible on this page in indexable HTML sections.
Is this lesson free?
Yes. CourseHive lessons and courses are available to learn online for free.
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