Building Neural Networks from Scratch Lecture 7 - Coding one neural network forward pass in Python (no loss)
Lecture 7 - Coding one neural network forward pass in Python (no loss) Transcript and Lesson Notes
In this lecture, we learn about implementing the entire forward pass (without loss) in Python. We will consider a 2 layer Neural Network with 2 inputs and 3 outputs. We will be using a ReLU activation function in the hid
Quick Summary
In this lecture, we learn about implementing the entire forward pass (without loss) in Python. We will consider a 2 layer Neural Network with 2 inputs and 3 outputs. We will be using a ReLU activation function in the hid
Key Takeaways
- Review the core idea: In this lecture, we learn about implementing the entire forward pass (without loss) in Python. We will consider a 2 layer Neural Network with 2 inputs and 3 outputs. We will be using a ReLU activation function in the hid
- Understand how lecture fits into Lecture 7 - Coding one neural network forward pass in Python (no loss).
- Understand how coding fits into Lecture 7 - Coding one neural network forward pass in Python (no loss).
- Understand how neural fits into Lecture 7 - Coding one neural network forward pass in Python (no loss).
- Understand how network fits into Lecture 7 - Coding one neural network forward pass in Python (no loss).
Key Concepts
Full Transcript
In this lecture, we learn about implementing the entire forward pass (without loss) in Python. We will consider a 2 layer Neural Network with 2 inputs and 3 outputs. We will be using a ReLU activation function in the hidden layer and Softmax activation function in the output layer. Google Colab Notebook: https://drive.google.com/file/d/1D9mPdUhW8N5pd6F5xlo2nAGwBQzfY6ip/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 7 - Coding one neural network forward pass in Python (no loss) about?
In this lecture, we learn about implementing the entire forward pass (without loss) in Python. We will consider a 2 layer Neural Network with 2 inputs and 3 outputs. We will be using a ReLU activation function in the hid
What key concepts are covered in this lesson?
The lesson covers lecture, coding, neural, network, forward.
What should I learn before Lecture 7 - Coding one neural network forward pass in Python (no loss)?
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, coding, neural, network.
Does this lesson include a transcript?
Yes. The full transcript is visible on this page in indexable HTML sections.
Is this lesson free?
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