Deep Learning Project: Training Classification Model | Introduction to Neural Networks (Lecture 26) скачать в хорошем качестве

Deep Learning Project: Training Classification Model | Introduction to Neural Networks (Lecture 26)

Welcome to the twenty-sixth lecture of my Deep Learning series! 🧠🔥 In this lecture, we bring everything together. We are moving away from abstract tensors and dummy data to build our first end-to-end Deep Learning project: Binary Classification. The goal of this video is to train a Neural Network to solve the classic "Moon Dataset" problem. We start by visualizing why this is hard (linear lines can't separate these shapes!) and then train a multi-layer perceptron (MLP) to create a complex, non-linear decision boundary. We also perform a critical benchmark: comparing the training time of our custom "from-scratch" library against PyTorch. You will see firsthand why libraries like PyTorch are essential for production—cutting training time from hours down to seconds. In this video, we cover: ✅ * The Moon Dataset:* We use sklearn.datasets to generate a non-linear dataset (two interleaving half-circles) to challenge our neural network. ✅ Data Visualization: We use matplotlib to plot our data and, crucially, to render the Decision Boundary. This allows us to visually see the "brain" of the network and how it classifies regions of space. ✅ Performance Showdown: We compare our custom Python implementation vs. PyTorch (which takes ~2 seconds). We discuss why PyTorch is so much faster (C++ backend, optimized matrix multiplication). ✅ Model Architecture: We define a deeper MLP (2 inputs → 16 neurons → 16 neurons → 1 output) using nn.Sequential to capture the non-linear nature of the dataset. ✅ Mini-Batch Gradient Descent: We implement a robust training loop using torch.randperm to feed data in batches (e.g., 50 samples at a time). This is a key technique for training on large datasets. ✅ Saving & Loading Models: We use Python’s pickle module to serialize (save) our trained weights and load them back later for inference without retraining. ✅ Inference Mode: Using torch.no_grad() to efficiently calculate predictions and visualize the final results. By the end of this lecture, you won't just have a trained model; you'll have a visual proof that your code works and a solid understanding of the full Machine Learning pipeline. Resources: 🔗 GitHub Repository (Code & Notes): https://github.com/gautamgoel962/Yout... 🔗 Follow me on Instagram:   / gautamgoel978   Subscribe and hit the bell icon! 🔔 Now that we have conquered binary classification, in the next videos, we will dive deeper into regularization, more complex loss functions, and building Language Models (Bigram). Let's keep building! 📉🚀 #PyTorch #DeepLearning #BinaryClassification #ScikitLearn #Matplotlib #NeuralNetworks #DataScience #PythonProgramming #MiniBatch #GradientDescent #ModelTraining #Visualization #SoftwareEngineering #HindiTutorial #AIProject