Why neural networks are so deep? (AlexNet - Explained)

Let's understand how neural networks became so deep and why they needed to be

ABOUT ME
⭕ Subscribe: https://www.youtube.com/c/CodeEmporiu...
📚 Medium Blog:   / dataemporium  
💻 Github: https://github.com/ajhalthor
👔 LinkedIn:   / ajay-halthor-477974bb  

RESOURCES
[1 📚] Slides used in the video: https://link.excalidraw.com/p/readonl...
[2 📚] Main paper that introduced AlexNet: https://proceedings.neurips.cc/paper_...
[3 📚] Great video that explains lateral inhibition:    • Photoreceptors, Receptive Fields, and Late...  


PLAYLISTS FROM MY CHANNEL
⭕ Reinforcement Learning:    • Reinforcement Learning 101  
Natural Language Processing:    • Natural Language Processing 101  
⭕ Transformers from Scratch:    • Natural Language Processing 101  
⭕ ChatGPT Playlist:    • ChatGPT  
⭕ Convolutional Neural Networks:    • Convolution Neural Networks  
⭕ The Math You Should Know :    • The Math You Should Know  
⭕ Probability Theory for Machine Learning:    • Probability Theory for Machine Learning  
⭕ Coding Machine Learning:    • Code Machine Learning  


MATH COURSES (7 day free trial)
📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML
📕 Calculus: https://imp.i384100.net/Calculus
📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStati...
📕 Bayesian Statistics: https://imp.i384100.net/BayesianStati...
📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra
📕 Probability: https://imp.i384100.net/Probability

OTHER RELATED COURSES (7 day free trial)
📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning
📕 Python for Everybody: https://imp.i384100.net/python
📕 MLOps Course: https://imp.i384100.net/MLOps
📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP
📕 Machine Learning in Production: https://imp.i384100.net/MLProduction
📕 Data Science Specialization: https://imp.i384100.net/DataScience
📕 Tensorflow: https://imp.i384100.net/Tensorflow

CHAPTERS
00:00 Introduction
00:15 Timeline of neural network research
01:52 Rise of GPUs and how it helped neural networks
02:18 ImageNet and hot it helped computer vision research
02:56 How AlexNet came to be
04:09 AlexNet architecture and training at a high level
05:53 ReLU activation and how to removes vanishing gradients
08:30 Training on multiple GPUs and how it speeds up performance
10:09 Local Response Normalization to mimic lateral inhibition
14:27 Overlapping pooling
15:15 Why do Deep networks overfit?
15:57 Data Augmentation and how it curbs overfitting
17:02 Dropout and how it curbs overfitting
19:32 Putting it all together
20:24 Quiz Time
21:22 Summary