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.
#PyTorch, #DeepLearning, #ArtificialNeuralNetwork, #ANN, #TrainTestSplit, #CPUGPUSetup, #PyTorchTutorial, #DeepLearningFromScratch, #AI, #MachineLearning, #TechEducation, #DataScience, #NeuralNetworks, #PyTorchForBeginners, #ModelImplementation
Auf dieser Seite können Sie das Online-Video 09-Implementing ANN Using PyTorch | Train/Test Split, CPU/GPU Setup, and Model Implementation mit der Dauer stunde minuten sekunde in guter Qualität ansehen, das der Benutzer SquareBrackets 01 September 2024 hochgeladen hat, den Link mit Freunden und Bekannten teilen, dieses Video wurde auf Youtube bereits 83 Mal angesehen und es wurde von 9 den Zuschauern gefallen. Viel Spaß beim Betrachtenden Zuschauern gefallen!
