Learn to Create Deep Learning Algorithms in Python

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Artificial intelligence is growing exponentially. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence. In the Udemy course “Deep Learning A-Z™: Hands-On Artificial Neural Networks”, you will work on Real-World datasets, to solve Real-World business problems.

Deep Learning A-Z™: Hands-On Artificial Neural Networks Course Content

Mastering Deep Learning is not just about knowing the intuition and tools, it’s also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That’s why in this course we are introducing six exciting challenges:

Churn Modelling Problem – to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information, if any individual customer will leave the bank or stay (customer churn).
Image Recognition – to create a Convolutional Neural Network that is able to detect various objects in images.
Stock Price Prediction – to create one of the most powerful Deep Learning models – Recurrent Neural Networks.
Fraud Detection – to create a Deep Learning model for a bank, given a dataset that contains information on customers applying for an advanced credit card.
Recommender System – to create a good recommender system for a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched, and the ratings go from 1 to 5.
Recommender System – to create a system able to predict the ratings of the movies the customers didn’t watch. Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.

The course comprises 121 Lectures organized into the following sections:

Welcome to the course

——————— Part 1 – Artificial Neural Networks ———————

ANN Intuition
Building an ANN
Homework Challenge – Should we say goodbye to that customer?
Evaluating, Improving and Tuning the ANN
Homework Challenge – Put me one step down on the podium

——————– Part 2 – Convolutional Neural Networks ——————–

Intuition
Building a CNN
Homework – What’s that pet ?
Evaluating, Improving and Tuning the CNN

———————- Part 3 – Recurrent Neural Networks ———————-

RNN Intuition
Building a RNN
Homework Challenge – Google Stock Price Prediction
Evaluating, Improving and Tuning the RNN

———————— Part 4 – Self Organizing Maps ————————

Building a SOM
Homework Challenge – Make a Hybrid Deep Learning model

—————————- Part 6 – AutoEncoders —————————-

Building an AutoEncoder

——————- Annex – Get the Machine Learning Basics ——————-

Regression & Classification Intuition
Data Preprocessing Template
Classification Template

Summary of Main Course Features

Instructors:
- Kirill Eremenko – Data Scientist
- Hadelin de Ponteves – AI Entrepreneur
- SuperDataScience Team – Helping Data Scientists Succeed SuperDataScience
Lectures: 182
On-demand video: 23 hours
Articles: 25
Includes:
- 30-Day Money-Back Guarantee
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion