PyTorch Deep Learning and Artificial Intelligence Description
This course caters to a diverse range of students, from beginners to experts, accommodating various skill levels.If you’ve recently completed my complimentary Numpy prerequisite, you already possess the necessary knowledge to seamlessly transition into this course. We’ll commence with fundamental machine learning models and progress toward cutting-edge concepts.
Throughout the journey, you’ll delve into major deep learning architectures, including Deep Neural Networks, Convolutional Neural Networks (for image processing), and Recurrent Neural Networks (for sequence data). The course is structured to provide a comprehensive learning experience, ensuring students at different proficiency levels find valuable insights and challenges.
Current projects include:
- Natural Language Processing (NLP)
- Recommender Systems
- Transfer Learning for Computer Vision
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning Stock Trading Bot
Requirements
- Know how to code in Python and Numpy
- For the theoretical parts (optional), understand derivatives and probability
What You’ll Learn in PyTorch Deep Learning and Artificial Intelligence course
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore’s Law using Code
- Transfer Learning to create state-of-the-art image classifiers
Who this course is for:
Beginners to advanced students who want to learn about deep learning and AI in PyTorch.This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
