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Practical Deep Learning with Keras and Python

Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training.
Instructor:
Mohammad Nauman
1,500 students enrolled
English [Auto-generated]
Be able to run deep learning models with Keras on Tensorflow backend
Stunning SUPPORT. I answer questions on the same day.
Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue)
Understand Deep Learning with minimal of math
Understand and code Convolutional Neural Networks as well as graph-based deep models involving residual connections and inception modules
Get tips on how to use Google's GPUs to speed up your experiments for free
Understand and use Keras' functional API to create models with multiple inputs and outputs

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems.

In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras — one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models such as:

  • Convolutional Neural Networks 
  • Residual Connections 
  • Inception Module

All with only a few lines of code. All the examples used in the course comes with starter code which will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck.

There is also a real-time chat system in place for students who enroll in this course. With a free signup, you get access to real-time chat with myself and fellow students who are working to complete this course (or have completed the course before you). We plan on creating this network of like-minded machine learning experts who can help each other out and collaborate on exciting ideas together.

What will I learn? 

  • Basics of machine learning with minimal math
  • A specialized but optional mathematics heavy talk that explains all the inner working of machine learning and deep learning 
  • Applying machine learning principles to solve a real-world case study that includes pre-processing and getting your data into the proper shape. (This case study comes from a real research work I have carried out recently)
  • Understand the often problematic shape issue that makes machine learning difficult to apply in real life 
  • Learn the details of ConvNets and graph-based machine learning models such as Residual Connections and Google’s Inception Module 
  • Use Keras’s functional API to create powerful models that will help you move way beyond the contents covered in this course 
  • Learn how to use Google’s GPUs to speed up your experiments for free
  • Tips on avoiding mistakes made by new-comers to the field and the best practices to get you to your goal with minimal effort 

About the instructor: 

  • Teacher and researcher by profession
  • PhD in Security and a PostDoc from Max Planck Institute for Software Systems, Germany
  • 17+ years of working with computers and 15+ years of teaching experience 
  • 3+ years of working extensively with deep learning. I worked with almost all the modern tools as soon as they were released

Target Audience:

Anyone who:

  • Wants to learn machine learning (this course is a soft introduction)  
  • Knows machine learning and wants to learn deep learning (this course focuses on deep learning
  • Knows deep learning but needs help applying their knowledge in practice (this is a very applied course
  • Comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples) 
  • Is a researchers or educator working in machine learning and wants to move from theory to practice

What you need to know:

  • Python basics (installation, if, loops, lists) – Everything else will be covered in the course
  • No machine learning background is assumed (but we keep the theory to a minimum)

Introduction

1
About the Instructor
2
Dive into Machine Learning
3
Making Predictions

A Bit of Theory

1
Machine Learning Pipeline
2
Regression
3
Binary and Multi-class Classification
4
Recap and a Link to More Theory

Installation and Setup

1
Environment setup for Windows (and some issues with it)
2
Environment setup for Mac and Linux

Say Hi to Keras

1
Data Preparation
2
Training and Testing

Real World Case Study: Predicting Protein Functions

1
Problem Description and Data View
2
Pre-processing the Data
3
Loading Data and Getting the Shapes Right
4
Train, Test Split
5
Shapes in Depth (or how not to have headaches for days)
6
Sequential Model
7
Functional API

Convolutional Neural Networks (CNN)

1
Basics and Rationale
2
CNN in Keras (or why Keras is better than your ML tool)
3
Pooling (and why it's not that important)
4
Dropout (and why you should always consider it)

Graph-based Models

1
Functional API for CNN
2
Inception Module
3
Residual Connections

Finishing Touches

1
Saving and Loading Model Weights
2
Parting Words

Extra Resources

1
Machine Learning Yearning Book (Free Download)
2
Bonus Lecture: Get all of my courses for up to 90% off
You can view and review the lecture materials indefinitely, like an on-demand channel.
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3 hours on-demand video
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Certificate of Completion