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[FreeCoursesOnline.Me] [MANNING] Deep Learning Crash Course [FCO]
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• 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!
Aabout 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.
Prerequisites
You need beginner to intermediate Python programming skills and some experience working with organized data files, such as databases or spreadsheets.
Wwhat you will learn
• 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
Aabout the instructor
Oliver Zeigermann - is a machine learning instructor at Hamburg University of Applied Sciences. He also works as a consultant, helping to take organizations to the next level with machine learning.
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FILE LIST
Filename
Size
01 - Why this course.mp4
90.2 MB
02 - Why Machine Learning.mp4
97.6 MB
03 - Our problem in the TensorFlow Playground.mp4
24.1 MB
04 - How does a neuron work.mp4
74.2 MB
05 - Drawing Decision Boundaries with a single neuron.mp4
21.9 MB
06 - Activation Functions.mp4
41.9 MB
07 - Fully Connected Feed Forward Networks.mp4
25.5 MB
08 - How does a network learn.mp4
41.4 MB
09 - Finding the sweet spot.mp4
16.3 MB
10 - Summary.mp4
12 MB
11 - Python Notebooks on Colab.mp4
30.5 MB
12 - Getting to know our data.mp4
30.2 MB
13 - Our first network with TensorFlow and the Keras API.mp4
53.4 MB
14 - Evaluating our model.mp4
53 MB
15 - Training a network with TensorFlow and the Keras API.mp4
59.5 MB
16 - Making our model more general.mp4
29.1 MB
17 - Summing up and saving our final model.mp4
27.7 MB
18 - Converting the Keras model for tensorflow.js.mp4
19.8 MB
19 - Gluing together our JavaScript application.mp4
18.1 MB
20 - Alternative - Hosting your model on Google Cloud ML.mp4
71 MB
21 - Alternative - Running on a dedicated Linux server.mp4
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