Application of Deep Neural Networks

About This Course/Certificate

About This workshop and the materials in this repo are for anyone who is interested in working with Data Science to produce high quality, working style! Check out follow course link if you think it is interested.

Course Link: Application of Deep Neural Networks

Course Learn Path

The courses enlisted as follows:

• C1-Python Preliminaries
• C2-Python for Machine Learning
• C3-TensorFlow and Keras for Neural Networks
• C4-Traning for Tabular Data
• C5-Regularization and Dropout
• C6-CNN for Vision
• C7-Generative Adversarial Networks(GANs)
• C8-Kaggle
• C9-Transfer Learning
• C10-Time Series in Keras
• C11-Natural Language Processing
• C12-Reinforcement Learning
• C13-Deployment and Monitoring

Course Description: 12 Chapter

Sponsor University : Washington University in St.Louis

Main Instructor:

1. Jeff Heaton

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.

Applied Learning Project

Tools: Jupyter / JupyterLab, GitHub,

Libraries: Keras

Projects:

Useful Resources

Video Reference: Youtube Video Reference
Github Project Reference: Github Repo Reference

Course Certificate

Total Hours: ~115 Hrs

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