Data

Introduction to Deep Learning Models

Deep Learning is at the heart of many of today's innovations from image recognition to natural language processing (NLP). This course will teach you how to train deep neural networks including: Fully Connected, Convolutional, and Recurrent Neural Networks.

Difficile

8 heures

Ce cours en libre accès vous intéresse ?

Commencer

From Tesla's self-driving cars, to cashier-free shopping, to Google Translate, neural networks are at the heart of so many of today's innovations. But how, exactly, do they work? What makes these neural networks so special? How can you apply these techniques to your own machine learning projects. If any of those questions have ever crossed your mind, then you have come to the right place!

Part 1 will explore the differences between machine learning and deep learning, all while framing the discussion around real-life uses-cases.

Parts 2 and 3 are where the magic happens. Anchored in a real-world scenario, you will learn how to train everything from a single neuron, to bigger, more complex networks such as Recurrent Neural Networks and Convolutional Neural Networks.

By the end of the course, you will have created a number of Jupyter Notebooks that you can keep as a reference for future projects - either on the AI Engineer Path or in your job as a Data Scientist!

Ready to get started? Then join me in the first chapter!

Meet Your Teacher!

Radu has always been passionate about problem solving, and more specifically, about building systems to help people reach their goals. This passion initially led him down a career in software engineering, but as he learned more and more about data science, he came to appreciate the power it had to solve bigger, more complex problems! Eventually, he founded Disruptive Engineering Ltd., an AI consulting firm helping businesses use AI to achieve their strategic objectives.

Objectifs pédagogiques

Train simple deep learning models
Train advanced deep learning models

Prérequis

Prerequisites:

Knowledge in machine learning, cross validation, and scoring machine learning algorithms
Knowledge about splitting datasets for machine learning
Knowledge in Python

If you are unfamiliar with these concepts, we highly encourage you to take the following course: Train a Supervised Machine Learning Model, Learn Python Basics for Data Analytics, and Use Python Libraries for Data Science