Neural Networks | Easiest Explanation for Papa | Anyone can learn AI | Basics of Deep Learning
If you appreciate the content and the hard work, Please 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 - https://www.youtube.com/@KeertiPurswani
𝐂𝐡𝐞𝐜𝐤𝐨𝐮𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐚𝐧𝐝 𝐞𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐋𝐈𝐕𝐄 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐡𝐞𝐫𝐞-
https://www.educosys.com
𝐍𝐨𝐭𝐞𝐬 𝐚𝐫𝐞 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐡𝐞𝐫𝐞 𝐟𝐨𝐫 𝐅𝐫𝐞𝐞 - https://register.educosys.com/new-courses/32-free-machine-learning-course
𝐄𝐝𝐮𝐜𝐨𝐬𝐲𝐬 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 - https://www.instagram.com/educosys
𝐄𝐝𝐮𝐜𝐨𝐬𝐲𝐬 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 - https://www.linkedin.com/company/98837223
You can also connect with me on-
𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 - https://www.linkedin.com/in/keertipurswani
𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 (for personal, raw and real side of my life) - https://www.instagram.com/keerti.purswani
Timestamps-
0:00 - Coming up
0:15 - Equation of line example
2:03 - Educosys GenAI
2:45 - Loss Functions
4:35 - Gradients
5:30 - How Learning of variables happen
5:49 - Intro to term NN
7:15 - NN VIsualization
9:27 - NN with one neuron in hidden layer
10:20 - Activation Functions
11:00 - Universal Approximation Theorem
12:14 - Papa’s Test
#softwaredevelopment #softwareengineer #machinelearningengineer #artificialintelligenceandmachinelearning
If you appreciate the content and the hard work, Please 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 – https://www.youtube.com/@KeertiPurswani
𝐂𝐡𝐞𝐜𝐤𝐨𝐮𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐚𝐧𝐝 𝐞𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐋𝐈𝐕𝐄 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐡𝐞𝐫𝐞-
https://www.educosys.com
𝐍𝐨𝐭𝐞𝐬 𝐚𝐫𝐞 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐡𝐞𝐫𝐞 𝐟𝐨𝐫 𝐅𝐫𝐞𝐞 – https://register.educosys.com/new-courses/32-free-machine-learning-course
𝐄𝐝𝐮𝐜𝐨𝐬𝐲𝐬 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 – https://www.instagram.com/educosys
𝐄𝐝𝐮𝐜𝐨𝐬𝐲𝐬 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 – https://www.linkedin.com/company/98837223
You can also connect with me on-
𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 – https://www.linkedin.com/in/keertipurswani
𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 (for personal, raw and real side of my life) – https://www.instagram.com/keerti.purswani
Timestamps-
0:00 – Coming up
0:15 – Equation of line example
2:03 – Educosys GenAI
2:45 – Loss Functions
4:35 – Gradients
5:30 – How Learning of variables happen
5:49 – Intro to term NN
7:15 – NN VIsualization
9:27 – NN with one neuron in hidden layer
10:20 – Activation Functions
11:00 – Universal Approximation Theorem
12:14 – Papa’s Test
#softwaredevelopment #softwareengineer #machinelearningengineer #artificialintelligenceandmachinelearning