| 1. Collision Avoidance Introduction.mp4 | 11.2 MB | ||
| 1. Deep Learning Hands-On Introduction.mp4 | 12.3 MB | ||
| 1. Deep Learning Introduction.mp4 | 14.6 MB | ||
| 1. Installation.mp4 | 35.8 MB | ||
| 1. Introduction to Computer Vision.mp4 | 10.6 MB | ||
| 1. Introduction to Python Libraries.mp4 | 18.1 MB | ||
| 1. Machine Learning Hands-On Introduction.mp4 | 16.7 MB | ||
| 1. What's Machine Learning.mp4 | 26 MB | ||
| 1. Why Learn Control Theory.mp4 | 86.5 MB | ||
| 1. Why This Course.mp4 | 127.8 MB | ||
| 10. Bias Vs Variance.mp4 | 36.6 MB | ||
| 10. PID Controller - Why is it use SO much.mp4 | 63.9 MB | ||
| 10. Webots too slow.html | 716.8 B | ||
| 11. Webots Code Explained.mp4 | 99.3 MB | ||
| 11. [Advanced] Paper PID Controller Design.html | 409.6 B | ||
| 11. [Advanced] Paper SVM.html | 716.8 B | ||
| 12. [Exercise] Your Line Following Algorithm!.html | 1.6 KB | ||
| 13. [Advanced] How to Read a Paper.mp4 | 106.3 MB | ||
| 14. [Advanced] Paper SIFT.html | 1.1 KB | ||
| 2. Control Systems Map.mp4 | 58.5 MB | ||
| 2. Creating a Dataset.mp4 | 56.1 MB | ||
| 2. Feature Engineering.mp4 | 31.8 MB | ||
| 2. How Computers See Images.mp4 | 42.6 MB | ||
| 2. How do Neural Networks Work.mp4 | 59.6 MB | ||
| 2. How to Approach This Course.mp4 | 26.4 MB | ||
| 2. Numpy.mp4 | 44.3 MB | ||
| 2. Ranging Sensors.mp4 | 51.1 MB | ||
| 2. Train, Predict & Evaluate.mp4 | 24.3 MB | ||
| 2. Types in Python.mp4 | 19.4 MB | ||
| 3. Cameras.mp4 | 17.4 MB | ||
| 3. HOG.mp4 | 103.4 MB | ||
| 3. How does a Neural Network Learn.mp4 | 39.8 MB | ||
| 3. Kernel & Convolution.mp4 | 54.3 MB | ||
| 3. List & Map.mp4 | 32.8 MB | ||
| 3. Make it Engaging.mp4 | 17.4 MB | ||
| 3. Matplotlib.mp4 | 37 MB | ||
| 3. Stability - Introduction.mp4 | 55.8 MB | ||
| 3. Training.mp4 | 37.2 MB | ||
| 3. Types of Machine Learning.mp4 | 21.7 MB | ||
| 4. Convolutional Neural Networks.mp4 | 56.1 MB | ||
| 4. Get the Course Code for Practical Lectures.html | 1.6 KB | ||
| 4. Image Processing with Kernels.mp4 | 59.1 MB | ||
| 4. ML for Self-Driving Cars.mp4 | 19.3 MB | ||
| 4. OpenCV.mp4 | 72 MB | ||
| 4. Operations.mp4 | 13.5 MB | ||
| 4. SVM.mp4 | 42.3 MB | ||
| 4. See it drive!.mp4 | 84.2 MB | ||
| 4. Simulation.mp4 | 109.9 MB | ||
| 4. Stability - Missing in Machine Learning.mp4 | 53.8 MB | ||
| 5. Code Example.mp4 | 53.9 MB | ||
| 5. My Solution.mp4 | 56.4 MB | ||
| 5. Open and Closed Loop Control.mp4 | 36.1 MB | ||
| 5. Other Libraries.mp4 | 32.3 MB | ||
| 5. Performance Metrics.mp4 | 30.5 MB | ||
| 5. Statements.mp4 | 51.1 MB | ||
| 5. Thresholding.mp4 | 41.5 MB | ||
| 5. [Exercise] Train it yourself!.html | 921.6 B | ||
| 6. Closed Loop Control - Cruise Control.mp4 | 60.5 MB | ||
| 6. Download the Dataset.html | 819.2 B | ||
| 6. Functions.mp4 | 17.3 MB | ||
| 6. Road Segmentation.mp4 | 59.7 MB | ||
| 6. [Advanced] AlexNet.html | 614.4 B | ||
| 6. [Exercise] Your Solution.html | 921.6 B | ||
| 6.1 S2V6 Functions.mp4 | 6.6 MB | ||
| 7. Code Explanation.mp4 | 76.4 MB | ||
| 7. Object Oriented Programming.html | 1.4 KB | ||
| 7. PID - Introduction.mp4 | 86.6 MB | ||
| 7. Path Planning.mp4 | 38.1 MB | ||
| 7. Why Webots.html | 716.8 B | ||
| 8. Classes.mp4 | 36.7 MB | ||
| 8. How to Install Webots in Windows.html | 1.8 KB | ||
| 8. PID Controller - Deep Dive.mp4 | 81.3 MB | ||
| 8. [Advanced] RRT Code.html | 614.4 B | ||
| 8. [Exercise] Modify the code.html | 921.6 B | ||
| 9. How to Install Webots in Linux.html | 2.3 KB | ||
| 9. Libraries Modules.mp4 | 29.4 MB | ||
| 9. PID Controller - How to Tune it.mp4 | 80.4 MB | ||
| 9. Useful ML Models.mp4 | 61.4 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 80 total files | |||
Machine Learning & Self-Driving Cars Bootcamp with Python
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.87 GB | Duration: 6h 30m
Combine the power of Machine Learning, Deep Learning and Computer Vision to make a Self-Driving Car!
What you'll learn
Learn how to apply Machine Learning algorithms to develop a Self-Driving Car from scratch
Simulate a Self-Driving car in a realistic environment using multiple techniques (Computer Vision, Convolution Neural Networks, ...)
Understand how Self Driving Cars work (sensors, actuators, speed control, ...)
Learn about Computer Vision in a practical way, starting from simple examples until you are able to create an algorithm to drive a Self-Driving Car
Gentle introduction to Machine Learning, all the key concepts are presented in an intuitive way
Explain why Deep Learning is such a powerful ch and use it to make the car drive like a human (Behavioural Cloning)
Code Deep Convolutional Neural Networks with Keras (the most popular library)
Build, train and evaluate multiple models, from classic Machine Learning to Deep Neural Networks
How to code in Python starting from the very beginning
Python libraires: NumPy, Sklearn (Scikit-Learn), Keras, OpenCV, Matplotlib
Requirements
No programming experience needed. You will learn everything you'll need to know.
| torrent name | size | uploader | age | seed | leech |
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| 779 MB | freecoursewb | 5 days | 1 | 25 | |
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Udemy - Spark Machine Learning Project (House Sale Price Prediction) Posted by
freecoursewb in Other
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1.7 GB | freecoursewb | 4 weeks | 9 | 4 |
| 3.4 GB | freecoursewb | 1 month | 19 | 5 | |
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| 1.9 GB | freecoursewb | 2 months | 9 | 1 |
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