Deep Learning Crash Course

Deep Learning Crash Course
Deep Learning Crash Course
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 54m | 807 MB

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 video 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, 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 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!

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.

Inside:

  • The basics of neural networks
  • Machine learning techniques using Scikit-learn, TensorFlow 2.0, and Keras
  • How to train a machine learning model and evaluate the results
  • Debugging and improving your model
  • Deployment in a production environment

You need beginner to intermediate Python programming skills and some experience working with organized data files, such as databases or spreadsheets.

Table of Contents

01 Why this course
02 Why Machine Learning
03 Our problem in the TensorFlow Playground
04 How does a neuron work
05 Drawing Decision Boundaries with a single neuron
06 Activation Functions
07 Fully Connected Feed Forward Networks
08 How does a network learn
09 Finding the sweet spot
10 Summary
11 Python Notebooks on Colab
12 Getting to know our data
13 Our first network with TensorFlow and the Keras API
14 Evaluating our model
15 Training a network with TensorFlow and the Keras API
16 Making our model more general
17 Summing up and saving our final model
18 Converting the Keras model for tensorflow.js
19 Gluing together our JavaScript application
20 Alternative – Hosting your model on Google Cloud ML
21 Alternative – Running on a dedicated Linux server
22 Summary