What you'll learn:
- Understand the basic concepts about neural network and how it works
- Use PyTorch for Linear Regression using Multilayer Perceptron (MLP)
- Use PyTorch for image classification using Deep Artificial Neural Network (ANN)
- Learn how to work with different data types such as tensors and arrays
- Use PyTorch for image classification using Convolutional Neural Network (CNN)
- Use PyTorch for time series prediction using Recurrent Neural Network (RNN)
Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.
Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.
In this course, I will teach you everything you need to start deep learning with PyTorch such as:
NumPy Crash Course
Pandas Crash Course
Neural Network Theory and Intuition
How to Work with Torchvision datasets
Convolutional Neural Network (CNN)
Long-Short Term Memory (LSTM)
and much more
Since this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.
By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.
