Course 7: Pytorch
- 2 months
- Intakes: Jan, Apr, Jun, Oct
Prerequisites
- Basic knowledge of linear algebra and calculus
- Programming experience – Python Language and data processing libraries (Numpy and Matplotlib)
- Neural network experience
Who is this course for?
- Students who complete the python and Basic AI and ML courses and want to advance more.
- Students who have not studied Pytorch for learning Artificial Intelligence and Machine Learning.
- People who want to get a Pytorch certificate for their career.
What you'll get after completing this course:
- Loading and normalizing datasets
- Building the model layers
- Automatic differentiation
- Learn about the optimization loop
- Load and run model predictions
- The full model building process
- Introduction to processing image data
- Training a simple dense neural network
- Use a convolutional neural network
- Train multi-layer convolutional neural network
- Use a pre-trained netwrok with transfer learning
- Solving vision problems with MobileNet
- Representing text as Tensors
- Bag of Words and TF-IDF
- Represent words with embeddings
- Capture patterns with recurrent neural networks
- Generate text with recurrent networks
- Understand audio data and concepts
- Audio transformations and visualizations
- Build the speech model
How to Apply?
- You Apply
Tell us a little about yourself and we’ll help with the rest. Our convenient online application tool only takes 10 minutes to complete.
- We Connect
After you submit your application, an admissions representative will contact you and will help you to complete the process.
- You Get Ready
Once you’ve completed your application and connected with an admissions representative, you’re ready to create your schedule.