09-Implementing ANN Using PyTorch | Train/Test Split, CPU/GPU Setup, and Model Implementation

In this video, we continue our deep dive into PyTorch by focusing on crucial aspects of deep learning: splitting data into training and testing sets, setting up PyTorch to run on CPU and GPU, and implementing an Artificial Neural Network (ANN) model from scratch.

00:00:00 - Train Test Split
00:02:24 - Setup CPU/GPU in PyTorch
00:05:28 - Building Simple ANN Model
00:19:40 - Creating Object of Model Class
00:21:23 - Easier way to create model

What You’ll Learn:
Train/Test Data Split: Understand the importance of splitting your data into training and testing sets and how to do it effectively using PyTorch.
CPU/GPU Setup in PyTorch: Learn how to set up PyTorch to run on CPU or GPU, optimizing your deep learning model for better performance.
Implementing ANN Model: Step-by-step guidance on implementing an ANN model using PyTorch, including defining layers, forward pass, and backpropagation.

Why Watch This Video?
Practical PyTorch Knowledge: Gain hands-on experience with PyTorch, focusing on real-world tasks like data splitting and model implementation.
Optimizing Deep Learning: Learn how to leverage the power of GPUs to accelerate your deep learning tasks.
Comprehensive Tutorial: Whether you're new to PyTorch or deepening your understanding, this video provides valuable insights and practical skills.

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