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https://github.com/DeepKnowledge1/pyt... Does Deep Learning still feel like "magic" to you? 🧙♂️✨ You write loss.backward() and optimizer.step(), and suddenly the model learns... but what is actually happening under the hood? If you can't explain the math, you can't fix the model when it breaks. In Module 2 of our Deep Learning series, we are going to do something hard but incredibly powerful: 1️⃣ We will build a Neural Network completely from scratch using nothing but Python and NumPy. No frameworks. Just raw math. 2️⃣ We will derive the gradients, write the Backpropagation algorithm manually, and watch the network learn. 3️⃣ THEN, we will rebuild the exact same network using PyTorch (torch.nn) to show you how modern frameworks automate the heavy lifting. By the end of this video, PyTorch won't be a black box anymore. It will be a tool you have mastered. 🚀 🧠 What You’ll Learn 🔹 Forward propagation step-by-step 🔹 ReLU and Sigmoid activations 🔹 Binary Cross-Entropy loss (manually implemented) 🔹 Backpropagation using the chain rule 🔹 Gradient descent parameter updates 🔹 Why manual gradients are hard at scale 🔹 How PyTorch computes gradients automatically 🔹 How to define models using nn.Module 🔹 How to use optimizers like SGD 🔹 Manual vs PyTorch performance comparison By the end of this video, loss.backward() will no longer feel like magic ✨ 👨💻 Code & Resources: 📂 Get the Notebook here: [Link to your GitHub/Colab] 🔗 Watch Module 1 (Tensors): [Link to previous video] 📚 Deep Learning Playlist: [Link to playlist] 💡 Pro Tip: Beginners: Focus on the flow of data (Input → Hidden → Output). Pros: Pay attention to how the cache variable in our manual code mimics the computation graph in PyTorch! 🔔 Subscribe for more Deep Learning tutorials! If this video helped clarify the "magic" behind neural nets, please Like, Share, and Drop a Comment below! It helps the channel grow! 🚀 🏷️ Best Related Hashtags (Lowercase) #pytorch #deeplearning #neuralnetworks #machinelearning #python #datascience #artificialintelligence #coding #programmer #math #gradientdescent #backpropagation #ai #learnpython #tech