| 1. Introduction.mp4 | 40.4 MB | ||
| 1. Introduction.vtt | 3.2 KB | ||
| 1. Overview.html | 307.2 B | ||
| 1. Overview.mp4 | 50.2 MB | ||
| 1. Overview.vtt | 307.2 B | ||
| 1. Python Crash Course Part 1 - Data Types.mp4 | 15.2 MB | ||
| 1. Python Crash Course Part 1 - Data Types.vtt | 1.3 KB | ||
| 10. Code 9 - Polynomial Regression.html | 819.2 B | ||
| 10. Error Function.mp4 | 41.6 MB | ||
| 10. Error Function.vtt | 4.9 KB | ||
| 10. Final Source Code.html | 5 KB | ||
| 10. Hough Transform II.mp4 | 114.7 MB | ||
| 10. Hough Transform II.vtt | 14.2 KB | ||
| 10. Membership Operators.mp4 | 13.8 MB | ||
| 10. Membership Operators.vtt | 2.6 KB | ||
| 10. Part 4 - Outro.mp4 | 2.5 MB | ||
| 10. Part 4 - Outro.vtt | 204.8 B | ||
| 10. Section 10 - Outro.mp4 | 5.9 MB | ||
| 10. Section 10 - Outro.vtt | 512 B | ||
| 10. Section 12 - Outro.mp4 | 9.9 MB | ||
| 10. Section 12 - Outro.vtt | 307.2 B | ||
| 10. Self Driving Car - Test 1.mp4 | 307.2 B | ||
| 10. Self Driving Car - Test 1.vtt | 307.2 B | ||
| 11. Code 10 - Behavioural Cloning.html | 10.1 KB | ||
| 11. Generator - Augmentation Techniques.mp4 | 307.2 B | ||
| 11. Generator - Augmentation Techniques.vtt | 307.2 B | ||
| 11. Mutability.mp4 | 33 MB | ||
| 11. Mutability.vtt | 4.1 KB | ||
| 11. Optimizing.mp4 | 164.5 MB | ||
| 11. Optimizing.vtt | 14.8 KB | ||
| 11. Section 11 - Conclusion.mp4 | 4.8 MB | ||
| 11. Section 11 - Conclusion.vtt | 409.6 B | ||
| 11. Sigmoid.mp4 | 61.7 MB | ||
| 11. Sigmoid.vtt | 7.4 KB | ||
| 12. Batch Generator.mp4 | 307.2 B | ||
| 12. Batch Generator.vtt | 307.2 B | ||
| 12. Mutability II.mp4 | 31.6 MB | ||
| 12. Mutability II.vtt | 3.6 KB | ||
| 12. Resource for upcoming video.html | 102.4 B | ||
| 12. Sigmoid Implementation (Code).mp4 | 90.7 MB | ||
| 12. Sigmoid Implementation (Code).vtt | 13.2 KB | ||
| 12. Simulation Output Results - Training Track.mp4 | 307.2 B | ||
| 12. Simulation Output Results - Training Track.vtt | 307.2 B | ||
| 12.1 test2.mp4.zip.zip | 28 MB | ||
| 13. Common Functions & Methods.mp4 | 46.8 MB | ||
| 13. Common Functions & Methods.vtt | 7.1 KB | ||
| 13. Finding Lanes on Video.mp4 | 82.8 MB | ||
| 13. Finding Lanes on Video.vtt | 7.1 KB | ||
| 13. Fit Generator.mp4 | 307.2 B | ||
| 13. Fit Generator.vtt | 307.2 B | ||
| 13. Simulation Output Results - Test Track.mp4 | 307.2 B | ||
| 13. Simulation Output Results - Test Track.vtt | 307.2 B | ||
| 13. Source code.html | 1 KB | ||
| 13.1 test2.mp4.mp4 | 31.9 MB | ||
| 14. Cross Entropy.mp4 | 62.7 MB | ||
| 14. Cross Entropy.vtt | 7.3 KB | ||
| 14. Final Source Code.html | 10.1 KB | ||
| 14. Source Code.html | 2.9 KB | ||
| 14. Tuples.mp4 | 23.1 MB | ||
| 14. Tuples.vtt | 3.3 KB | ||
| 15. Cross Entropy (Code).mp4 | 61.2 MB | ||
| 15. Cross Entropy (Code).vtt | 8.5 KB | ||
| 15. Outro.mp4 | 307.2 B | ||
| 15. Outro.vtt | 307.2 B | ||
| 15. Part 5 - Conclusion.mp4 | 10.2 MB | ||
| 15. Part 5 - Conclusion.vtt | 819.2 B | ||
| 15. Sets.mp4 | 19.6 MB | ||
| 15. Sets.vtt | 2.7 KB | ||
| 16. Dictionaries.mp4 | 35.3 MB | ||
| 16. Dictionaries.vtt | 4.8 KB | ||
| 16. Source Code.html | 1.2 KB | ||
| 17. Compound Data Structures.mp4 | 20.2 MB | ||
| 17. Compound Data Structures.vtt | 2.4 KB | ||
| 17. Gradient Descent.mp4 | 45.2 MB | ||
| 17. Gradient Descent.vtt | 3.4 KB | ||
| 18. Gradient Descent (Code).mp4 | 75.7 MB | ||
| 18. Gradient Descent (Code).vtt | 9.5 KB | ||
| 18. Part 1 - Outro.mp4 | 3.6 MB | ||
| 18. Part 1 - Outro.vtt | 409.6 B | ||
| 19. Part 2 - Control Flow.mp4 | 11.5 MB | ||
| 19. Part 2 - Control Flow.vtt | 1 KB | ||
| 19. Recap.mp4 | 17.7 MB | ||
| 19. Recap.vtt | 2.6 KB | ||
| 2. Anaconda Distribution.mp4 | 26.3 MB | ||
| 2. Anaconda Distribution.vtt | 2.9 KB | ||
| 2. Arithmetic Operations.mp4 | 25.4 MB | ||
| 2. Arithmetic Operations.vtt | 4.8 KB | ||
| 2. Code 1 - Computer Vision.html | 2.9 KB | ||
| 2. Collecting Data.mp4 | 282.4 MB | ||
| 2. Collecting Data.vtt | 307.2 B | ||
| 2. Convolutions & MNIST.mp4 | 90 MB | ||
| 2. Convolutions & MNIST.vtt | 7.1 KB | ||
| 2. Image needed for the next lesson.html | 102.4 B | ||
| 2. Implementation.mp4 | 128.9 MB | ||
| 2. Implementation.vtt | 307.2 B | ||
| 2. Intro to Keras.mp4 | 21.4 MB | ||
| 2. Intro to Keras.vtt | 2.4 KB | ||
| 2. MNIST Dataset.mp4 | 71 MB | ||
| 2. MNIST Dataset.vtt | 5.7 KB | ||
| 2. Machine Learning.mp4 | 37 MB | ||
| 2. Machine Learning.vtt | 3.5 KB | ||
| 2. Non-Linear Boundaries.mp4 | 71.1 MB | ||
| 2. Non-Linear Boundaries.vtt | 5.9 KB | ||
| 2. Softmax.mp4 | 141.7 MB | ||
| 2. Softmax.vtt | 14.4 KB | ||
| 2. Traffic Signs Starter Code.html | 1.5 KB | ||
| 2. Vector Addition - Arrays vs Lists.mp4 | 86.8 MB | ||
| 2. Vector Addition - Arrays vs Lists.vtt | 12.7 KB | ||
| 2.1 Image.zip.zip | 759.1 KB | ||
| 20. If, else.mp4 | 27.2 MB | ||
| 20. If, else.vtt | 4.4 KB | ||
| 20. Source Code.html | 1.9 KB | ||
| 21. Part 6 - Conclusion.mp4 | 9.7 MB | ||
| 21. Part 6 - Conclusion.vtt | 716.8 B | ||
| 21. elif.mp4 | 49 MB | ||
| 21. elif.vtt | 6.5 KB | ||
| 22. Complex Comparisons.mp4 | 29.5 MB | ||
| 22. Complex Comparisons.vtt | 4.7 KB | ||
| 23. For Loops.mp4 | 38.5 MB | ||
| 23. For Loops.vtt | 6.6 KB | ||
| 24. For Loops II.mp4 | 15.1 MB | ||
| 24. For Loops II.vtt | 2.9 KB | ||
| 25. While Loops.mp4 | 20.2 MB | ||
| 25. While Loops.vtt | 3.1 KB | ||
| 26. Break.mp4 | 19.7 MB | ||
| 26. Break.vtt | 3.5 KB | ||
| 27. Part 2 - Outro.mp4 | 4.5 MB | ||
| 27. Part 2 - Outro.vtt | 409.6 B | ||
| 28. Part 3 - Functions.mp4 | 11.4 MB | ||
| 28. Part 3 - Functions.vtt | 1.1 KB | ||
| 29. Functions.mp4 | 31.6 MB | ||
| 29. Functions.vtt | 5.3 KB | ||
| 3. Architecture.mp4 | 126 MB | ||
| 3. Architecture.vtt | 11.7 KB | ||
| 3. Code 2 - Intro to Neural Networks.html | 1.9 KB | ||
| 3. Convolutional Layer.mp4 | 229.8 MB | ||
| 3. Convolutional Layer.vtt | 20 KB | ||
| 3. Cross Entropy.mp4 | 81.9 MB | ||
| 3. Cross Entropy.vtt | 10.3 KB | ||
| 3. Downloading Data.mp4 | 130.6 MB | ||
| 3. Downloading Data.vtt | 307.2 B | ||
| 3. Final Source Code.html | 819.2 B | ||
| 3. Jupyter Notebooks.mp4 | 41.9 MB | ||
| 3. Jupyter Notebooks.vtt | 6.1 KB | ||
| 3. Linear Regression.mp4 | 46.6 MB | ||
| 3. Linear Regression.vtt | 4.8 KB | ||
| 3. Loading Images.mp4 | 30.7 MB | ||
| 3. Loading Images.vtt | 4.8 KB | ||
| 3. Multidimensional Arrays.mp4 | 96.8 MB | ||
| 3. Multidimensional Arrays.vtt | 11.8 KB | ||
| 3. Preprocessing Images.mp4 | 330.4 MB | ||
| 3. Preprocessing Images.vtt | 43.1 KB | ||
| 3. Starter Code.html | 512 B | ||
| 3. Train & Test.mp4 | 132 MB | ||
| 3. Train & Test.vtt | 14.3 KB | ||
| 3. Variables.mp4 | 27.7 MB | ||
| 3. Variables.vtt | 5.5 KB | ||
| 3.1 Image.zip.zip | 759.1 KB | ||
| 3.1 Traffic Signs Starter Project.ipynb.zip.zip | 1.5 KB | ||
| 30. Scope.mp4 | 13.2 MB | ||
| 30. Scope.vtt | 1.8 KB | ||
| 31. Doc Strings.mp4 | 19.6 MB | ||
| 31. Doc Strings.vtt | 2.6 KB | ||
| 32. Lambda & Higher Order Functions.mp4 | 28.4 MB | ||
| 32. Lambda & Higher Order Functions.vtt | 6.5 KB | ||
| 33. Part 3 - Outro.mp4 | 8.8 MB | ||
| 33. Part 3 - Outro.vtt | 819.2 B | ||
| 4. Balancing Data.mp4 | 74.7 MB | ||
| 4. Balancing Data.vtt | 307.2 B | ||
| 4. Classification.mp4 | 82.1 MB | ||
| 4. Classification.vtt | 8.6 KB | ||
| 4. Code 3 - Intro to Keras.html | 2.3 KB | ||
| 4. Convolutions II.mp4 | 79.8 MB | ||
| 4. Convolutions II.vtt | 8.6 KB | ||
| 4. Feedforward Process.mp4 | 88.9 MB | ||
| 4. Feedforward Process.vtt | 9.9 KB | ||
| 4. Grayscale.mp4 | 47.2 MB | ||
| 4. Grayscale.vtt | 4.7 KB | ||
| 4. Hyperparameters.mp4 | 81.2 MB | ||
| 4. Hyperparameters.vtt | 7.4 KB | ||
| 4. Implementation.mp4 | 245.3 MB | ||
| 4. Implementation.vtt | 35.2 KB | ||
| 4. Keras Models.mp4 | 175.3 MB | ||
| 4. Keras Models.vtt | 24.1 KB | ||
| 4. Numeric Data Types.mp4 | 23.5 MB | ||
| 4. Numeric Data Types.vtt | 4.1 KB | ||
| 4. One Dimensional Slicing.mp4 | 27.8 MB | ||
| 4. One Dimensional Slicing.vtt | 3.9 KB | ||
| 4. Section 13 - Conclusion.mp4 | 5.2 MB | ||
| 4. Section 13 - Conclusion.vtt | 307.2 B | ||
| 4. Text Editor.mp4 | 29.1 MB | ||
| 4. Text Editor.vtt | 3.2 KB | ||
| 4. leNet Implementation.mp4 | 129.4 MB | ||
| 4. leNet Implementation.vtt | 20.3 KB | ||
| 5. Code 4 - Deep Neural Networks.html | 1.9 KB | ||
| 5. Error Function.mp4 | 54.1 MB | ||
| 5. Error Function.vtt | 5.5 KB | ||
| 5. Fine-tuning Model.mp4 | 117 MB | ||
| 5. Fine-tuning Model.vtt | 15.3 KB | ||
| 5. Gaussian Blur.mp4 | 30.6 MB | ||
| 5. Gaussian Blur.vtt | 3.4 KB | ||
| 5. Implementation Part 1.mp4 | 194 MB | ||
| 5. Implementation Part 1.vtt | 34.5 KB | ||
| 5. Keras - Predictions.mp4 | 144.4 MB | ||
| 5. Keras - Predictions.vtt | 20.3 KB | ||
| 5. Linear Model.mp4 | 86.4 MB | ||
| 5. Linear Model.vtt | 7.8 KB | ||
| 5. Outro.mp4 | 5.5 MB | ||
| 5. Outro.vtt | 716.8 B | ||
| 5. Pooling.mp4 | 161.9 MB | ||
| 5. Pooling.vtt | 16 KB | ||
| 5. Reshaping.mp4 | 23.4 MB | ||
| 5. Reshaping.vtt | 3.5 KB | ||
| 5. Source Code.html | 2.3 KB | ||
| 5. String Data Types.mp4 | 42 MB | ||
| 5. String Data Types.vtt | 5.7 KB | ||
| 5. Training & Validation Split.mp4 | 65.7 MB | ||
| 5. Training & Validation Split.vtt | 307.2 B | ||
| 6. Backpropagation.mp4 | 65.4 MB | ||
| 6. Backpropagation.vtt | 6.6 KB | ||
| 6. Booleans.mp4 | 24.2 MB | ||
| 6. Booleans.vtt | 4.6 KB | ||
| 6. Canny Edge Detection.mp4 | 42.6 MB | ||
| 6. Canny Edge Detection.vtt | 4.7 KB | ||
| 6. Code 5 - Multiclass Classification.html | 2.3 KB | ||
| 6. Fully Connected Layer.mp4 | 77.9 MB | ||
| 6. Fully Connected Layer.vtt | 7.1 KB | ||
| 6. Implementation Part 2.mp4 | 155.9 MB | ||
| 6. Implementation Part 2.vtt | 21.4 KB | ||
| 6. Multidimensional Slicing.mp4 | 49.2 MB | ||
| 6. Multidimensional Slicing.vtt | 7.3 KB | ||
| 6. Perceptrons.mp4 | 50.7 MB | ||
| 6. Perceptrons.vtt | 4.7 KB | ||
| 6. Preprocessing Images.mp4 | 161.7 MB | ||
| 6. Preprocessing Images.vtt | 307.2 B | ||
| 6. Resources Needed for Testing.html | 2.1 KB | ||
| 6. Section 9 - Outro.mp4 | 5.4 MB | ||
| 6. Section 9 - Outro.vtt | 409.6 B | ||
| 6. Source Code.html | 2.2 KB | ||
| 7. Code 6 - MNIST Image Recognition.html | 3.7 KB | ||
| 7. Code Implementation.mp4 | 204.2 MB | ||
| 7. Code Implementation.vtt | 29.4 KB | ||
| 7. Defining Nvidia Model.mp4 | 198.5 MB | ||
| 7. Defining Nvidia Model.vtt | 307.2 B | ||
| 7. Manipulating Array Shapes.mp4 | 47.8 MB | ||
| 7. Manipulating Array Shapes.vtt | 8.4 KB | ||
| 7. Methods.mp4 | 20.7 MB | ||
| 7. Methods.vtt | 3.3 KB | ||
| 7. Part 7 - Outro.mp4 | 4.8 MB | ||
| 7. Part 7 - Outro.vtt | 409.6 B | ||
| 7. Region of Interest.mp4 | 49.3 MB | ||
| 7. Region of Interest.vtt | 7.8 KB | ||
| 7. Resource for upcoming video.html | 204.8 B | ||
| 7. Starter Code.html | 6.4 KB | ||
| 7. Testing.mp4 | 63.1 MB | ||
| 7. Testing.vtt | 307.2 B | ||
| 7. Weights.mp4 | 25.3 MB | ||
| 7. Weights.vtt | 2.3 KB | ||
| 8. Binary Numbers & Bitwise_and.mp4 | 91.8 MB | ||
| 8. Binary Numbers & Bitwise_and.vtt | 10.3 KB | ||
| 8. Code 7 - Convolutional Neural Networks.html | 5.1 KB | ||
| 8. Code Implementation I.mp4 | 254.5 MB | ||
| 8. Code Implementation I.vtt | 32.4 KB | ||
| 8. Drive.py code.html | 2 KB | ||
| 8. Fit Generator.mp4 | 159.8 MB | ||
| 8. Fit Generator.vtt | 307.2 B | ||
| 8. Implementation Part 3.mp4 | 75.3 MB | ||
| 8. Implementation Part 3.vtt | 11 KB | ||
| 8. Lists.mp4 | 35.8 MB | ||
| 8. Lists.vtt | 5.7 KB | ||
| 8. Matrix Multiplication.mp4 | 34.3 MB | ||
| 8. Matrix Multiplication.vtt | 4.2 KB | ||
| 8. Project - Initial Stages.mp4 | 78.2 MB | ||
| 8. Project - Initial Stages.vtt | 12.9 KB | ||
| 8. Source Code.html | 1.9 KB | ||
| 8.1 Initial Stages.ipynb.zip.zip | 1.3 KB | ||
| 9. Code 8 - Traffic Sign Classification.html | 7.3 KB | ||
| 9. Code Implementation II.mp4 | 213.4 MB | ||
| 9. Code Implementation II.vtt | 26.4 KB | ||
| 9. Final Source Code.html | 3.7 KB | ||
| 9. Flask & Socket.io.mp4 | 307.2 B | ||
| 9. Flask & Socket.io.vtt | 307.2 B | ||
| 9. Hough Transform.mp4 | 132.6 MB | ||
| 9. Hough Transform.vtt | 12.1 KB | ||
| 9. Sample Code for Initial Stages.html | 614.4 B | ||
| 9. Section 8 - Conclusion.mp4 | 6 MB | ||
| 9. Section 8 - Conclusion.vtt | 512 B | ||
| 9. Slicing.mp4 | 55.6 MB | ||
| 9. Slicing.vtt | 7.8 KB | ||
| 9. Stacking.mp4 | 82.3 MB | ||
| 9. Stacking.vtt | 12.9 KB | ||
| 9.1 Initial Stages.ipynb.zip.zip | 1.3 KB | ||
| ▲ 315 total files | |||
What Will I Learn?
Learn to apply Computer Vision and Deep Learning techniques to build automotive-related algorithms
Understand, build and train Convolutional Neural Networks with Keras
Simulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer Vision
Train a Deep Learning Model that can identify between 43 different Traffic Signs
Learn to use essential Computer Vision techniques to identify lane lines on a road
Learn to build and train powerful Neural Networks with Keras
Understand Neural Networks at the most fundamental perceptron-based level
Requirements
A working computer
No experience required!
Description
Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector.
Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today.
Learn & Master Deep Leaning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a “learn by doing” style to create this amazing course.
You’ll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.
By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company.
This course will show you how to:
Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.
Learn to train a Perceptron-based Neural Network to classify between binary classes.
Learn to train Convolutional Neural Networks to identify between various traffic signs.
Train Deep Neural Networks to fit complex datasets.
Master Keras, a power Neural Network library written in Python.
Build and train a fully functional self driving car to drive on its own!
No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.
This course also comes with all the source code and friendly support in the Q&A area.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
|
Udemy - ISO 26262 Automotive Functional Safety - The Blueprint Method Posted by
freecoursewb in Other
|
1.9 GB | freecoursewb | 4 days | 1 | 17 |
|
Udemy - Databricks Playbook - Operate, Govern and Defend the Lakehouse Posted by
freecoursewb in Other
|
2.3 GB | freecoursewb | 5 days | 1 | 6 |
| 426.3 MB | freecoursewb | 5 days | 33 | 10 | |
| 473.8 MB | freecoursewb | 1 week | 16 | 5 | |
| 3.7 GB | freecoursewb | 2 weeks | 5 | 7 |
All Comments