How can you benefit from deep learning?
- Accurately analyze customer buying habits so you can make great recommendations
- Verify digital identity to protect customers from theft and fraud
- Create intelligent voice assistants for speech-commanded shopping and customer service
- Expand your customer base with automatic translation
In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning. This powerful data analysis technique mimics the way humans process information to identify patterns in your data and learn from them. With Oliver Zeigermann’s crystal-clear video instruction and the hands-on exercises in this video course, you’ll get started in deep learning using open-source Python-friendly tools like scikit-learn and Keras, and TensorFlow 2.0 (soon to be officially released with exciting new updates!). If you’re ready to take the fast path to deep learning, Deep Learning Crash Course is for you!
About the subject
Deep learning is an emerging artificial intelligence (AI) technique that uses sophisticated analysis structures called neural networks to make accurate associations within a set of data. In particular, deep learning systems can learn by processing raw data without human-coded rules or domain knowledge. These systems are particularly adept at language and image classification, where a pattern may represent an abstract idea like feeling, intent, or even the general concept of what a cat or a dog looks like. These systems are also excellent for making predictions, such as how customers might behave or long-range weather forecasts. There’s also awesome potential for medical image analysis, highly-customized therapy for patients with developmental challenges, turning open surgeries into minimally-invasive ones, and better disaster recovery!About the video
With an emphasis on simplicity, Deep Learning Crash Course teaches you to build machine learning models, the part of a system that makes classifications and predictions. You’ll also learn how to apply algorithms that train the model to improve based on the data it encounters. Your video guide Oliver Zeigermann launches your learning with a spotlight on how deep learning is different from other programming and data analysis techniques. You’ll work through a complete project and learn to use the most popular Python-based deep learning tools, including scikit-learn, Keras, and TensorFlow 2.0. All the tools are free and open source. The incredible machine learning library Keras has a minimalistic, instantly-comfortable API that handles most of the math, so you’ll get the maximum return on your time. As you work your way through this practical video course, you’ll gain skills like training a neural network, creating and executing TensorFlow code, encoding your data, and making your model more general. By the end, you’ll know how to evaluate your results, debug and improve your model, and deploy it for production.
Table of Contents detailed table of contents
Introduction
Why this course?
Why Machine Learning?
How good can you guess?
Basic Concepts of Deep Supervised Machine Learning
Our problem in the TensorFlow Playground
How does a neuron work?
Drawing Decision Boundaries with a single neuron
What can you do with a single Neuron?
Activation Functions
Fully Connected Feed Forward Networks
How many neurons do you need to decently separate the two classes?
How does a network learn?
Finding the sweet spot
Train a network on a complex shape
Summary
Classification with TensorFlow and the Keras API
Python Notebooks on Colab
Getting to know Notebooks and Colab
Getting to know our data
Getting familiar with Python and plotting libraries
Our first network with TensorFlow and the Keras API
What is being learned?
Evaluating our model
Understanding Generalization
Training a network with TensorFlow and the Keras API
Train the model
Making our model more general
Apply regularizations to your model
Summing up and saving our final model
Bringing your Machine Learning Model into Production
Converting the Keras model for tensorflow.js
Gluing together our JavaScript application
Make the Risk Calculator run on your machine
Alternative: Hosting your model on Google Cloud ML
Alternative: Running on a dedicated Linux server
Summary
Prerequisites
You need beginner to intermediate Python programming skills and some experience working with organized data files, such as databases or spreadsheets.placing your order...
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What's a liveVideo?
What's a liveVideo?
- Quality lessons from expert programmers.
- Rich, interactive transcripts for navigation.
- Exercise driven learning.
What's a liveVideo?
