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[FreeCoursesOnline.Me] [MANNING] Grokking Deep Learning In Motion [FCO]
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Author : Beau Carnes Publisher : Manning Publications Released : 8/2018 Duration : 5h 38m Language : English Torrent Contains : 44 Files, 1 Folders Course Source : https://www.manning.com/livevideo/grokking-deep-learning-in-motion
Video Description
Despite being one of the biggest technical leaps in AI in decades, building an understanding in deep learning doesn't mean you need a math degree. All it takes is the right intuitive approach, and you'll be writing your own neural networks in pure Python in no time!
About the subject
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. To really get the most out of deep learning, you need to understand it inside and out, but where do you start? This liveVideo course is the perfect jumping off point!
About the video
Grokking Deep Learning in Motion is a new liveVideo course that takes you on a journey into the world of deep learning. Rather than just learn how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!
Professional instructor Beau Carnes breaks deep learning wide open, drawing together his expertise in video instruction and Andrew Trask's unique, intuitive approach from Grokking Deep Learning! As you move through this course, you’ll learn the fundamentals of deep learning from a unique standing! Using Python, as well as Jupyter Notebooks, you’ll get stuck right in to the basics of neural prediction and learning, and teach your algorithms to visualize things like different weights. Throughout, you’ll train your neural network to be smarter, faster, and better at its job in a variety of ways, ready for the real world!
Packed with great animations and explanations that bring the world of deep learning to life in a way that just makes sense, Grokking Deep Learning in Motion is exactly what anyone needs to build an intuitive understanding of one of the hottest techniques in machine learning.
This liveVideo also works perfectly alongside the original Grokking Deep Learning by Andrew Trask, bringing his unique way to teaching to life.
Prerequisites
This liveVideo course is perfect for anyone with high school-level math and basic programming skills with a language like Python. Experience with Calculus is helpful but NOT required.
What you will learn
• The differences between deep and machine learning • An introduction to neural prediction • Building your first deep neural network • The importance of visualization tools • Memorization vs Generalization • Modeling probabilities and non-linearities
About the instructor
liveVideo instructor Beau Carnes is a software developer and a recognized authority in software instruction. Besides teaching in-person workshops and classes, Beau has recently joined the team at freeCodeCamp as their lead video instructor, helping to teach over 2 million people around the world to code. Beau also teaches Manning's best-selling video course, Algorithms in Motion.
Table of Contents
• INTRODUCING DEEP LEARNING • FUNDAMENTAL CONCEPTS • INTRODUCTION TO NEURAL PREDICTION • INTRODUCTION TO NEURAL LEARNING • LEARNING MULTIPLE WEIGHTS AT A TIME • BUILDING YOUR FIRST "DEEP" NEURAL NETWORK • HOW TO PICTURE NEURAL NETWORKS • LEARNING SIGNAL AND IGNORING NOISE • MODELING PROBABILITIES AND NON-LINEARITIES • CONCLUSION.
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FILE LIST
Filename
Size
0. Websites you may like/How you can help Team-FTU.txt
237 B
01 - Introduction.mp4
91 MB
02 - What you need to get started.mp4
75.2 MB
03 - What is Deep Learning and Machine Learning.mp4
82.6 MB
04 - Supervised vs. unsupervised learning.mp4
74.9 MB
05 - Parametric vs. non-parametric learning.mp4
100.2 MB
06 - Making a prediction.mp4
90.4 MB
07 - What does a Neural Network do.mp4
45.7 MB
08 - Multiple inputs.mp4
95.2 MB
09 - Multiple outputs and stacking predictions.mp4
74.2 MB
10 - Primer on NumPy.mp4
63.6 MB
11 - Compare and learn.mp4
82.6 MB
12 - Why measure error.mp4
58.6 MB
13 - Hot and cold learning.mp4
85.7 MB
14 - Gradient descent.mp4
80.7 MB
15 - Learning with gradient decent.mp4
82.4 MB
16 - The secret to learning.mp4
83 MB
17 - How to use a derivative to learn.mp4
105.8 MB
18 - Alpha.mp4
77.2 MB
19 - Gradient descent learning with multiple inputs.mp4
80.3 MB
20 - Several steps of learning.mp4
46 MB
21 - Gradient descent with multiple outputs.mp4
39.3 MB
22 - Visualizing weight values.mp4
92.6 MB
23 - The streetlight problem.mp4
80.7 MB
24 - Building our neural network.mp4
96.5 MB
25 - Up and down pressure.mp4
138 MB
26 - Correlation and backpropagation.mp4
92.7 MB
27 - Linear vs. non-linear.mp4
68 MB
28 - Our first 'deep' neural network.mp4
89.1 MB
29 - Simplifying.mp4
86.6 MB
30 - Simplified visualization.mp4
73.1 MB
31 - Seeing the network predict.mp4
80.2 MB
32 - 3-layer network on MNIST.mp4
121.9 MB
33 - Overfitting in Neural Networks.mp4
84.4 MB
34 - Regularization - Early Stopping and Dropout.mp4
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