Image Classification | Complete Project | Deep Learning with PyTorch

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In this end-to-end deep learning project, we build an image classification model using a Multi-Layer Perceptron (MLP) using PyTorch. MLP, though traditionally used for structured data, can also be effective for image classification when paired with appropriate feature extraction techniques.

In this video, we dive deep into creating data pre-processing pipeline (creating dataset and data loader objects), model building, model training, evaluation and hyper parameter tuning for image classification using Multi-Layer Perceptron (MLP). Learn how to optimize your deep learning models for better accuracy and performance.

Learn the step-by-step process of:
• Dataset Overview.
• Data PreProcessing : Creating dataset objects, normalizing the data.
• Data Partitioning and creating data loader objects.
• Setting up the MLP architecture in PyTorch
• Model training.
• Optimizing the model using loss functions and optimizers
• Training with GPU (CUDA).
• Hyper Parameter Tuning, model evaluation and performance metrics.

If you're learning Machine Learning, Deep Learning, or AI, this video will provide you with a solid foundation to implement your own models. Don't forget to hit like, comment, and subscribe to keep learning with me!
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🎯 Who is this for?
Perfect for beginners and intermediates in deep learning who want a structured and practical approach to building AI models. Whether you're prepping for a data science interview or looking to build your own projects, mastering deep learning algorithms will set a strong foundation for more advanced machine learning techniques.

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#hyperparametertuning #deeplearningprojects

Time breaks:
00:00 Dataset Overview
05:21 Data Preprocessing : Creating dataset objects
07:58 Data Partitioning
11:46 Creating DataLoader objects.
13:01 Read Image data.
13:50 Shape of X data & display image.
17:05 Reading data using DataLoader object.
19:10 Data Pre-processing Overview.
22:00 Creating custom class.
23:35 Define forward pass.
28:34 Defining network.
29:46 Creating model object, optimizer and loss function.
32:06 Generate predictions, applying Softmax.
35:19 Implementing model training process
38:35 Model Evaluation
41:48 Tracking metrics for each epoch
43:09 Visualization
47:13 Device : CUDA
48:42 Move tensor and model objects to GPU
52:10 Custom data loader class
55:18 Model Training process using GPUs
57:57 Hyper parameter tuning - Context
01:04:38 Model training using Hyper-parameter grid
01:06:48 Model Performance metrics
01:08:12 Conclusion and next steps

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