Deep Learning Fundamentals: The Neural Network

In this course, you'll explore the fundamental unit of deep learning, the Neuron, and understand how it works. You'll also compare and contrast traditional machine learning (ML) and deep learning models. You'll identify how deep learning models excel in automated feature extraction from raw data to tackle complex tasks with the power of vast datasets. 

Next, you'll explore the diverse neural network architectures designed for specific data types. You'll learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data.
Finally, you'll explore how neural networks handle natural language processing, and how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You'll also study reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.

This course contains 11 modules (Total Duration: 1hr, 20mins)

1. Course Overview
2. Understanding Deep Learning
3. Understanding Neurons
4. Training a Neural Network
5. Convolutional Neural Networks (CNNs)
6. Recurrent Neural Networks (RNNs)
7. Natural Language Processing (NLP)
8. Attention-based Models & Transformers
9. Generative Adversarial Networks (GANs)
10. Reinforcement Learning
11. Course Summary

This is a Digital Product!
After course purchase, you'll receive your credentials via email to access the online Student Portal to view your course material.

Our online courses are digital products that will be delivered via online access through our Student Portal.

Digital products are ineligible for refunds or exchanges.

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