Ementorhub

What you'll learn

Build and train neural networks using PyTorch for various tasks.

Implement classification models with multi-class, multi-label datasets, and CNNs for image and audio classification.

Apply object detection techniques using the YOLO algorithm.

Explore neural style transfer, transfer learning, and implement RNNs and LSTM networks.

Skills you'll gain

Deep Learning

Artificial Intelligence and Machine Learning (AI/ML)

Data Processing

Predictive Modeling

PyTorch (Machine Learning Library)

Artificial Neural Networks

Image Analysis

Applied Machine Learning

Classification And Regression Tree (CART)

Algorithms

Computer Vision

There are 7 modules in this course

Moving forward, the course delves into Convolutional Neural Networks (CNNs) for image and audio classification. You'll discover the architecture of CNNs, implement image preprocessing techniques, and develop both binary and multi-class image classification models. Additionally, the course covers advanced topics like layer calculations and the application of CNNs in audio classification, ensuring you gain a holistic understanding of these powerful models. The journey continues with a focus on object detection, where you'll explore accuracy metrics, labeling formats, and the YOLO (You Only Look Once) algorithm. Practical coding exercises will guide you through the setup, data preparation, model training, and inference processes. Furthermore, you'll delve into neural style transfer, pre-trained networks, transfer learning, and recurrent neural networks (RNNs), including hands-on coding with LSTM networks. This course is designed for data scientists, AI professionals, and developers eager to master neural networks using PyTorch. Prerequisites include experience with Python and a foundational understanding of machine learning and deep learning concepts.

CNN: Image Classification

CNN: Audio Classification

CNN: Object Detection

Style Transfer

Pre-Trained Networks and Transfer Learning

Recurrent Neural Networks