Krish Naik Profile

Krish Naik

  • @krishnaik06
  • 836.000 Subscriber Subscriber
  • 1739 Videos Videos
I am the cofounder of iNeuron and my experience is pioneering in machine learning, deep learning, and computer vision,an educator, and a mentor, with over 10 years' experience in the industry.
Visit

About Krish Naik

Krish Naik's YouTube channel, where the world of machine learning, deep learning, and artificial intelligence comes alive!With an impressive following of 836,000 subscribers and an extensive library featuring 1739 videos, Krish Naik, the co-founder of iNeuron, shares his pioneering expertise in the fields of machine learning, deep learning, and computer vision.As an industry veteran, educator, and mentor with over 10 years of experience, Krish Naik brings a wealth of knowledge to his audience.

On this channel, he dives into various topics in machine learning, deep learning, and AI, presenting real-world problem scenarios with clarity and insight.Krish has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums, showcasing his commitment to spreading knowledge within the tech community.His main aim is to make everyone familiar with the world of ML and AI.

Subscribe to Krish Naik's channel to stay updated on the latest in machine learning and artificial intelligence.As a technology enthusiast, Krish provides all these videos for free and promises to continue delivering interesting and valuable content in the ever-evolving landscape of ML and AI.

Latest Videos

How the primary visual cortex processes information based on experiments by Hubel and Wiesel. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Original Paper from Hubel and Wiesel's experiment: https://pmc.ncbi.nlm.nih.gov/articles/PMC1363130/pdf/jphysiol01298-0128.pdf [2 📚] Demo from the experiment: https://www.youtube.com/watch?v=jw6nBWo21Zk [3 📚] Some images of cortical magnification and explanations of LGN, Primary Visual Cortex: https://www.cns.nyu.edu/~david/courses/perception/lecturenotes/V1/lgn-V1.html [4 📚] Wiesel explaining the experiment himself: https://www.youtube.com/watch?v=aqzWy-zALzY PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 Definition 00:19 How information travels from retina to brain 01:30 What does the primary visual cortex see? 02:55 How does the primary visual cortex process visual information? 05:54 Simple cells 08:40 Complex cells 10:19 What features are detected in each layer? 11:48 Quiz Time 13:00 Summary
Primary Visual Cortex: How brain processes what we see
We talk about center surround receptive fields in the human brain. Linked to computer vision. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Main paper in 1953 that discovered these center surround receptive fields: https://journals.physiology.org/doi/abs/10.1152/jn.1953.16.1.37 [2 📚] Video demo presented in video: https://www.youtube.com/watch?v=ZR7LzRAXNSw&list=LL&index=2 [3 📚] Summary notes on receptive fields: https://www.memcode.com/courses/1965 PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 Definition 03:22 Types of center surround receptive fields 05:15 Demo of receptive fields 09:35 Quiz Time 10:37 Summary
Receptive Fields - Explained
Let's talk about how visual information propagates from retina to the brain. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Description of retinal cells: https://www.imaios.com/en/e-anatomy/anatomical-structures/bipolar-cell-133706380# [2 📚] A nice video on Visual pathways: https://www.youtube.com/watch?v=ZmOL_6wJf4E [3 📚] Good explanation of how light does not stimulate non-photo receptor cells: https://www.reddit.com/r/Mcat/comments/skhbud/comment/hvneour/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Evolution of Neural Networks: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_DIV3TJkrS1wlq-G9G-yA2 ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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
Visual Pathway - Explained
Let's take a look at the structure of the human eye. This is the inspiration for modern computer vision today. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Khan academy structure of eye: https://www.youtube.com/watch?v=7HGxjs_aYyg [2 📚] If a point sized image is formed on a retina, why don't we see the world as points? https://qr.ae/pAIwGJ [3 📚] Diagram descriptions of the eye: https://www.reddit.com/r/Mcat/comments/15p9r26/depictions_of_eye_optics_are_misleading_tips_to/?rdt=39195 PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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
Vision: Structure of the eye - Explained!
Reinforcement Learning 101 ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1 📚] Reinforcement Learning book: http://incompleteideas.net/book/RLbook2020.pdf [2 📚] Paradigms of ML: https://idapgroup.com/blog/types-of-machine-learning-out-there/ [3 📚] ChatGPT blog: https://openai.com/blog/chatgpt [4 📚] Model Free vs Model Based RL: https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html [5 📚] Original Code for MAB: https://github.com/ankonzoid/LearningX/blob/master/classical_RL/multiarmed_bandit/multiarmed_bandit.py [6 📚] My code with some more comments: https://github.com/ajhalthor/multi-armed-bandit [7 📚] Monte Carlo Video referenced: https://www.youtube.com/watch?v=mECgLyCzTQU PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 TIMESTAMPS 00:00 Part 1: Elements of Reinforcement Learning 13:17 Part 2: Multi Armed Bandits 23:30 Part 3: Markov Decision Process 31:25 Part 4 Bellman Equation 40:14 Part 5: Q Learning 51:51 Part 6: On policy vs Off Policy 01:04:24 Part 7: Monte Carlo in RL 01:13:55 Part 8: Deep Q Networks 01:21:05 Part 9: Proximal Policy Optimization (PPO) 01:30:25 Part 10: RLHF
Reinforcement Learning: Zero to Hero
RESOURCES [1 📚 ] A logical calculus of the ideas immanent in neuron activity (McCulloch-Pitts artificial neuron): https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf [2 📚] The perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf [3 📚] An adaptive “Adaline” neuron using chemical “memistors”: https://isl.stanford.edu/~widrow/papers/t1960anadaptive.pdf [4 📚] Neural networks and physical systems with emergent collective computational abilities (Hopfield nets): https://pmc.ncbi.nlm.nih.gov/articles/instance/346238/pdf/pnas00447-0135.pdf [5 📚] A learning algorithm for Boltzmann Machines: https://web.archive.org/web/20110718022336/http://learning.cs.toronto.edu/~hinton/absps/cogscibm.pdf [6 📚] Learning representations by back propagating errors: http://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf SUPPLEMENTAL RESOURCES: [1 📚] Action potential - a nice video explanation: https://www.youtube.com/watch?v=BbUcWbtVjT4 [2 📚 ] Nice notes with MP neurons: https://marlin.life.utsa.edu/mcculloch-and-pitts.html [3 📚] Synaptic Plasticity: https://www.youtube.com/watch?v=-wakk5Iwqj8 [4 📚 ] Cell assembly in hebbian learning: http://www.scholarpedia.org/article/Cell_assemblies [5 📚] Perceptron demo: https://sergedesmedt.github.io/MathOfNeuralNetworks/PerceptronLearningMath.html#learn_perceptron_learningrule_animation [6 📚] Perceptron convergence theorem explained: https://www.youtube.com/watch?v=kObhWlqIeD8&t=250s [7 📚] A symbolic analysis of relay and switching circuits: https://www.cs.virginia.edu/~evans/greatworks/shannon38.pdf [8 📚 ] Geoffrey Hinton’s lecture on Hopfield Nets: https://www.youtube.com/watch?v=IP3W7cI01VY&list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0&index=11 [9 📚] Gibbs sampling used in learning in Boltzmann machines (video by Rithvikmath): https://www.youtube.com/watch?v=MNHIbOqH3sk [10 📚] Lecture notes on the mathematics behind Boltzmann machines: https://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/slides/lec19.pdf [11 📚] Cauchy’s paper on gradient descent: http://www.kurims.kyoto-u.ac.jp/EMIS/journals/DMJDMV/vol-ismp/40_lemarechal-claude.pdf [12 📚] Hinton explaining the mechanics of back propagation introduced by Rumelhart, Williams and himself: https://www.youtube.com/watch?v=VCT1N0EsGj0 [13 📚 ] Reverse mode automatic differentiation (like back propagation): https://rufflewind.com/2016-12-30/reverse-mode-automatic-differentiation ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PLAYLISTS FROM MY CHANNEL ⭕ Deep Learning 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_NwyY_PeSYrYfsvHZnHGPU ⭕ Natural Language Processing 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 0:00 Introduction 00:25 1. The artificial neuron 19:46 2. Perceptron 42:12 3. ADALINE 59:01 4. Hopfield Networks 1:18:57 5. Boltzmann Machines 1:42:20 6. Multilayer Perceptron 1:55:25 7. Evolution of Back propagation 2:22:27 Conclusion
Evolution of Neural Networks: Zero to Hero
Let's understand the evolution of back propagation. Who created it? Last video on multiplayer perceptron: https://youtu.be/poNXEd3jCwI RESOURCES [1 📚] 1847, Cauchy invented gradient descent. Here is the English rendition of this work with comments: http://www.kurims.kyoto-u.ac.jp/EMIS/journals/DMJDMV/vol-ismp/40_lemarechal-claude.pdf [2 📚] Hinton explaining the mechanics of back propagation introduced by Rumelhart, Williams and himself: https://www.youtube.com/watch?v=VCT1N0EsGj0 [3 📚] Proof that gradient vector points in the direction of steepest ascent: https://sootlasten.github.io/2017/gradient-steepest-ascent/ [4 📚] Online 3D plotter: https://c3d.libretexts.org/CalcPlot3D/index.html [5 📚] 1986 paper that is the most famous implementation of back propagation: https://www.semanticscholar.org/paper/Learning-representations-by-back-propagating-errors-Rumelhart-Hinton/052b1d8ce63b07fec3de9dbb583772d860b7c769 [6 📚] Henry J Kelly’s 1960 paper on determining the Optimal flight path and hence developing their own version of back propagation: https://gwern.net/doc/statistics/decision/1960-kelley.pdf [7 📚] Backstory of back propagation: https://yuxi-liu-wired.github.io/essays/posts/backstory-of-backpropagation [8 📚] Tourist guide to differential equations by 3blue1brown: https://www.youtube.com/watch?v=p_di4Zn4wz4 [9 📚] Khan Academy on derivatives, directional derivatives: https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/gradient-and-directional-derivatives/v/gradient [10 📚] A nice tutorial on reverse mode automatic differentiation: https://rufflewind.com/2016-12-30/reverse-mode-automatic-differentiation [11 📚 ] Everything you need to know about control theory. Amazing resource: https://youtu.be/lBC1nEq0_nk?si=-Jkh7PZl0bjY6LGE [12 📚] Thesis by Paul Werbos in 1974 (one of the first successful implementations of a back propagation learning procedure on a multi-layered perceptron): https://gwern.net/doc/ai/nn/1974-werbos.pdf [13 📚] How the adjoint method is similar to the reverse-mode Automatic differentiation: https://github.com/fancompute/workshop-invdesign/issues/3 [14 📚] The 1969 book on optimal control by Bryson and Ho: http://e.guigon.free.fr/rsc/book/BrysonHo75.pdf [15 📚 ] The adjoint method of gradient calculation: https://www.youtube.com/watch?v=Yiz92Ekn7vU [16 📚] Nice sheet on method of Lagrange multipliers (this is used in the adjoint state method): https://www.cs.toronto.edu/~fleet/courses/C11/Handouts/LagrangeMultipliers.pdf ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PLAYLISTS FROM MY CHANNEL ⭕ Deep Learning 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_NwyY_PeSYrYfsvHZnHGPU ⭕ Natural Language Processing 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 0:00 Why perceptron convergence learning could no longer be used 2:00 Why Analytical methods are infeasible 4:00 How numerical methods like gradient descent are feasible 7:00 Gradient vector and direction of steepest ascent 13:58 An efficient way to compute gradients 17:32 What is "backward" in back propagation and why 18:48 How this efficient gradient compute method is used in neural networks 20:08 Who invented backprop 23:22 Quiz Time 24:37 Summary
Evolution of back propagation: Who invented it?
Let's talk about the multi-layer perceptron RESOURCES [1 📚] Main paper (1986): http://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf [2 📚] Limitations of perceptrons: https://www.youtube.com/watch?v=16qJwiH-FdE [3 📚] Main perceptron paper: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf [4 📚] Perceptron demo: https://sergedesmedt.github.io/MathOfNeuralNetworks/PerceptronLearningMath.html#learn_perceptron_learningrule_animation [5 📚] Perceptron convergence theorem (to prove that a linear decision boundary can be found in a finite number of steps if such a boundary exists): https://www.youtube.com/watch?v=kObhWlqIeD8 ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PLAYLISTS FROM MY CHANNEL ⭕ Deep Learning 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_NwyY_PeSYrYfsvHZnHGPU ⭕ Natural Language Processing 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 0:00 Introducing the Perceptron 2:22 Perceptron vs Multi-layer perceptron 3:37 Difference 1: Hidden units 5:59 Difference 2: Non-linear units 8:02 Difference 3: New learning algorithm (back propagation) 11:48 Quiz Time 12:33 Summary
Mutli layer perceptron - Explained!
Let's talk about Boltzmann Machines RESOURCES [1 📚] Main paper: https://web.archive.org/web/20110718022336/http://learning.cs.toronto.edu/~hinton/absps/cogscibm.pdf [2 📚] Quick summary: https://www.cs.toronto.edu/~hinton/csc321/readings/boltz321.pdf [3 📚] Geoffry Hinton’s lectures (From 11 c): https://www.youtube.com/watch?v=IP3W7cI01VY&list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0&index=11 [4 📚] Another easier to understand summary : https://www.engati.com/glossary/boltzmann-machine [5 📚] University of Toronto :https://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/slides/lec19.pdf [6 📚] Rithvikmath has amazing videos on Gibb sampling (used here to get sample states from the model and use in our learning procedure) : https://www.youtube.com/watch?v=MNHIbOqH3sk [7 📚] Boltzmann Distribution: https://en.wikipedia.org/wiki/Boltzmann_distribution ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PLAYLISTS FROM MY CHANNEL ⭕ Deep Learning 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_NwyY_PeSYrYfsvHZnHGPU ⭕ Natural Language Processing 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ 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/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 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 0:00 Introduction 0:13 Pass 1: What is Boltzmann Machine? 4:03 Quiz 1 5:07 Pass 2: How does Boltzmann Machine work? 7:00 How the network learns the probability distribution? 14:22 Quiz 2 16:00 Energy landscape 15:19 Why is it "Boltzmann" machine? 16:28 Stochastic neuron probability function 19:50 How to derive the learning rule 20:28 How long does training happen? 21:24 Quiz 3 22:16 Summary
Boltzmann Machine - Explained!
Load More...

Links

youtube
Krish Hindi Data Science
youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw
Discord
discord.gg/Ca3P7AZ5re
Linkedin
in.linkedin.com/in/naikkrish
Instagram
instagram.com/krishnaik06
Twitter
twitter.com/Krishnaik06

Channel Details

internet
twitter.com/Krishnaik06
group
836.000 Subscriber
play-button
1739 Videos
click
84.429.003 Views
info
Joined 11.02.2012
India

Join the AI revolution and explore the world of artificial intelligence. Stay connected with us.

About

Resources

  • Latest AI Tools

Follow Us

Instagram

Copyright ©2025 Ai-Pedia