Backpropagation explained | Part 4 - Calculating the gradient

We're now on number 4 in our journey through understanding backpropagation. In our last video, we focused on how we can mathematically express certain facts about the training process. Now we're going to be using these expressions to help us differentiate the loss of the neural network with respect to the weights. Recall from our video that covered the intuition for backpropagation, that, for stochastic gradient descent to update the weights of the network, it first needs to calculate the gradient of the loss with respect to these weights. And calculating this gradient, is exactly what we'll be focusing on in this video. We're first going to start out by checking out the equation that backprop uses to differentiate the loss with respect to weights in the network. We'll see that this equation is made up of multiple terms, so next we'll break down and focus on each of these terms individually. Lastly, we'll take the results from each term and combine them to obtain the final result, which will be the gradient of the loss function. 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:58 Agenda 01:28 Derivative Calculations 05:45 Calculation Breakdown - First term 07:36 Calculation Breakdown - Second term 08:52 Calculation Breakdown - Third term 11:56 Summary 13:56 Collective Intelligence and the DEEPLIZARD HIVEMIND 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👉 Check out the website for more learning material: 🔗 https://deeplizard.com 💻 ENROLL TO GET DOWNLOAD ACCESS TO CODE FILES 🔗 https://deeplizard.com/resources 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 🧠 Use code DEEPLIZARD at checkout to receive 15% off your first Neurohacker order 👉 Use your receipt from Neurohacker to get a discount on deeplizard courses 🔗 https://neurohacker.com/shop?rfsn=6488344.d171c6 👀 CHECK OUT OUR VLOG: 🔗 https://youtube.com/deeplizardvlog ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Tammy Mano Prime Ling Li 🚀 Boost collective intelligence by sharing this video on social media! 👀 Follow deeplizard: Our vlog: https://youtube.com/deeplizardvlog Facebook: https://facebook.com/deeplizard Instagram: https://instagram.com/deeplizard Twitter: https://twitter.com/deeplizard Patreon: https://patreon.com/deeplizard YouTube: https://youtube.com/deeplizard 🎓 Deep Learning with deeplizard: Deep Learning Dictionary - https://deeplizard.com/course/ddcpailzrd Deep Learning Fundamentals - https://deeplizard.com/course/dlcpailzrd Learn TensorFlow - https://deeplizard.com/course/tfcpailzrd Learn PyTorch - https://deeplizard.com/course/ptcpailzrd Natural Language Processing - https://deeplizard.com/course/txtcpailzrd Reinforcement Learning - https://deeplizard.com/course/rlcpailzrd Generative Adversarial Networks - https://deeplizard.com/course/gacpailzrd 🎓 Other Courses: DL Fundamentals Classic - https://deeplizard.com/learn/video/gZmobeGL0Yg Deep Learning Deployment - https://deeplizard.com/learn/video/SI1hVGvbbZ4 Data Science - https://deeplizard.com/learn/video/d11chG7Z-xk Trading - https://deeplizard.com/learn/video/ZpfCK_uHL9Y 🛒 Check out products deeplizard recommends on Amazon: 🔗 https://amazon.com/shop/deeplizard 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ ❤️ Please use the knowledge gained from deeplizard content for good, not evil.