Neural Networks for Data Professionals: A Comprehensive Introduction

Overview

In the ever-evolving world of data science and artificial intelligence, mastering neural networks is a crucial skill for data professionals. Inspired by the human brain, these complex systems have revolutionized big data approaches, leading to breakthroughs in fields like image recognition and natural language processing.

This course, Neural Networks for Data Professionals:

A Comprehensive Introduction, will enable you to demystify and effectively utilize neural networks in your data projects.

We'll start with foundational concepts, including neuron and layer structures, different types of neural networks such as feedforward and recurrent, and key mechanisms like activation functions and backpropagation algorithms.

Next, you'll explore practical aspects of designing, training, and deploying neural networks. This includes creating a network architecture from scratch, selecting the appropriate input and output layers, and implementing hidden layers to optimize model performance.

You'll also learn to choose the right activation and loss functions, train your network with a dataset, and fine-tune hyperparameters for optimal performance.

Finally, we’ll cover advanced techniques to fine-tune and optimize neural networks for real-world applications. Topics include regularization, dropout, and batch normalization to prevent overfitting, adjusting learning rates for efficient training, utilizing transfer learning and pre-trained models, and interpreting loss and learning curves.

Upon completing this course, you'll possess the skills and knowledge needed to confidently build, deploy, and optimize neural networks for various data-driven applications, advancing your career in data science or expanding your skill set in this cutting-edge field.

University:

Provider:

Pluralsight

Categories:

Artificial Intelligence Courses, Neural Networks Courses, Data Science Courses, Hyperparameter Tuning Courses

Taught by

Pratheerth Padman

Subjects