Ementorhub

What you'll learn

Identify and recall deep learning foundations and applications

Explain how to develop and train neural network models

Use techniques to evaluate and optimize model performance

Assess the effectiveness of CNNs for image processing and semantic segmentation

Skills you'll gain

Shiny (R Package)

Tensorflow

Unsupervised Learning

Data Processing

Regression Analysis

Deep Learning

Dimensionality Reduction

PyTorch (Machine Learning Library)

Time Series Analysis and Forecasting

Image Analysis

Artificial Intelligence and Machine Learning (AI/ML)

Machine Learning Methods

Applied Machine Learning

Classification And Regression Tree (CART)

Artificial Neural Networks

Statistical Machine Learning

Predictive Modeling

Interactive Data Visualization

There are 8 modules in this course

Practical aspects include neural network layers, activation functions, and performance metrics in model evaluation. Through hands-on coding labs, you'll cover regression, classification, and convolutional neural networks (CNNs), building and fine-tuning models, understanding loss functions, and using optimizers for accuracy. Emphasis is on frameworks like TensorFlow and PyTorch for developing robust neural networks. The course concludes with specialized topics such as autoencoders, transfer learning, and recurrent neural networks (RNNs). Interactive labs and projects will apply knowledge to complex data analysis, time-series prediction, and creating web applications with Shiny. Ideal for data scientists, machine learning engineers, and AI enthusiasts, prerequisites include Python proficiency and basic machine learning knowledge.

Deep Learning: Regression

Deep Learning: Classification

Deep Learning: Convolutional Neural Networks

Deep Learning: Autoencoders

Deep Learning: Transfer Learning and Pretrained Networks

Deep Learning: Recurrent Neural Networks

Shiny