[FreeCourseSite.com] Udemy - Autonomous Cars Deep Learning and Computer Vision in Python - 磁力管家

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文档个数：188个文档
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收录时间：2021-03-24
最近下载：2025-05-15
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9. Artificial Neural Networks/10. Example 1 - Build Multi-layer perceptron for binary classification.mp4 402.9 MB
8. Machine Learning Part 2/6. [Activity] Detecting Cars Using SVM - Part #2.mp4 214.0 MB
11. Deep Learning and Tensorflow Part 2/8. [Activity] Build a CNN to Classify Traffic Siigns - part 2.mp4 183.8 MB
6. Computer Vision Basics Part 3/11. Histogram of Oriented Gradients (HOG).mp4 177.7 MB
4. Computer Vision Basics Part 1/9. [Activity] Convert RGB to HSV color spaces and mergesplit channels.mp4 175.0 MB
9. Artificial Neural Networks/4. ANN Training and dataset split.mp4 158.6 MB
11. Deep Learning and Tensorflow Part 2/7. [Activity] Build a CNN to Classify Traffic Signs.mp4 157.9 MB
3. Python Crash Course [Optional]/7. Introduction to Seaborn.mp4 153.8 MB
2. Introduction to Self-Driving Cars/1. A Brief History of Autonomous Vehicles.mp4 153.0 MB
5. Computer Vision Basics Part 2/9. Hough transform theory.mp4 148.4 MB
4. Computer Vision Basics Part 1/2. Humans vs. Computers Vision system.mp4 141.9 MB
10. Deep Learning and Tensorflow Part 1/3. [Activity] Building a Logistic Classifier with Deep Learning and Keras.mp4 141.1 MB
9. Artificial Neural Networks/1. Introduction What are Artificial Neural Networks and how do they learn.mp4 134.0 MB
8. Machine Learning Part 2/5. Project Solution Detecting Cars Using SVM - Part #1.mp4 125.5 MB
9. Artificial Neural Networks/2. Single Neuron Perceptron Model.mp4 125.5 MB
4. Computer Vision Basics Part 1/1. What is computer vision and why is it important.mp4 124.5 MB
5. Computer Vision Basics Part 2/11. Project Solution Hough transform to detect lane lines in an image.mp4 122.7 MB
8. Machine Learning Part 2/7. [Activity] Project Solution Detecting Cars Using SVM - Part #3.mp4 122.5 MB
4. Computer Vision Basics Part 1/8. Color Spaces.mp4 119.2 MB
9. Artificial Neural Networks/6. Code to build a perceptron for binary classification.mp4 117.0 MB
9. Artificial Neural Networks/8. Code to Train a perceptron for binary classification.mp4 115.6 MB
7. Machine Learning Part 1/8. [Activity] Decision Trees In Action.mp4 108.7 MB
9. Artificial Neural Networks/11. Example 2 - Build Multi-layer perceptron for binary classification.mp4 107.3 MB
11. Deep Learning and Tensorflow Part 2/6. [Activity] Improving our CNN's Topology and with Max Pooling.mp4 107.2 MB
5. Computer Vision Basics Part 2/2. [Activity] Code to perform rotation, translation and resizing.mp4 107.0 MB
4. Computer Vision Basics Part 1/12. Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp4 103.6 MB
4. Computer Vision Basics Part 1/3. what is an image and how is it digitally stored.mp4 103.3 MB
7. Machine Learning Part 1/1. What is Machine Learning.mp4 101.0 MB
7. Machine Learning Part 1/6. [Activity] Logistic Regression In Action.mp4 97.5 MB
6. Computer Vision Basics Part 3/3. Template Matching - Find a Truck.mp4 94.6 MB
5. Computer Vision Basics Part 2/5. Image cropping dilation and erosion.mp4 92.0 MB
4. Computer Vision Basics Part 1/4. [Activity] View colored image and convert RGB to Gray.mp4 90.3 MB
3. Python Crash Course [Optional]/5. Introduction to Pandas.mp4 90.2 MB
3. Python Crash Course [Optional]/6. Introduction to MatPlotLib.mp4 89.2 MB
9. Artificial Neural Networks/7. Backpropagation Training.mp4 88.3 MB
4. Computer Vision Basics Part 1/10. Convolutions - Sharpening and Blurring.mp4 88.2 MB
11. Deep Learning and Tensorflow Part 2/3. [Activity] Classifying Images with a Simple CNN, Part 1.mp4 87.6 MB
5. Computer Vision Basics Part 2/8. [Activity] Code to define the region of interest.mp4 84.2 MB
6. Computer Vision Basics Part 3/1. Image Features and their importance for object detection.mp4 82.8 MB
8. Machine Learning Part 2/2. [Activity] Naive Bayes in Action.mp4 82.6 MB
5. Computer Vision Basics Part 2/6. [Activity] Code to perform Image cropping dilation and erosion.mp4 80.7 MB
6. Computer Vision Basics Part 3/5. Corner detection – Harris.mp4 80.6 MB
8. Machine Learning Part 2/1. Bayes Theorem and Naive Bayes.mp4 79.7 MB
5. Computer Vision Basics Part 2/10. [Activity] Hough transform – practical example in python.mp4 79.5 MB
5. Computer Vision Basics Part 2/1. Image Transformation - Rotations, Translation and Resizing.mp4 79.2 MB
1. Environment Setup and Installation/1. Introduction.mp4 78.5 MB
10. Deep Learning and Tensorflow Part 1/1. Intro to Deep Learning and Tensorflow.mp4 78.1 MB
8. Machine Learning Part 2/4. [Activity] Support Vector Classifiers in Action.mp4 78.0 MB
9. Artificial Neural Networks/9. Two and Multi-layer Perceptron ANN.mp4 74.5 MB
11. Deep Learning and Tensorflow Part 2/1. Convolutional Neural Networks (CNN's).mp4 74.3 MB
5. Computer Vision Basics Part 2/4. [Activity] Perform non-affine image transformation on a traffic sign image.mp4 72.0 MB
3. Python Crash Course [Optional]/1. Python Basics Whitespace, Imports, and Lists.mp4 71.5 MB
4. Computer Vision Basics Part 1/14. [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam.mp4 70.8 MB
9. Artificial Neural Networks/5. Practical Example - Vehicle Speed Determination.mp4 70.8 MB
11. Deep Learning and Tensorflow Part 2/4. [Activity] Classifying Images with a Simple CNN, Part 2.mp4 70.4 MB
4. Computer Vision Basics Part 1/11. [Activity] Convolutions - Sharpening and Blurring.mp4 69.3 MB
7. Machine Learning Part 1/2. Evaluating Machine Learning Systems with Cross-Validation.mp4 69.2 MB
10. Deep Learning and Tensorflow Part 1/2. Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding..mp4 67.0 MB
4. Computer Vision Basics Part 1/7. What are the challenges of color selection technique.mp4 65.5 MB
7. Machine Learning Part 1/7. Decision Trees and Random Forests.mp4 64.5 MB
1. Environment Setup and Installation/2. Install Anaconda, OpenCV, Tensorflow, and the Course Materials.mp4 64.5 MB
1. Environment Setup and Installation/3. Test your Environment with Real-Time Edge Detection in a Jupyter Notebook.mp4 64.3 MB
6. Computer Vision Basics Part 3/12. [Activity] Code to perform HOG Feature extraction.mp4 64.0 MB
5. Computer Vision Basics Part 2/3. Image Transformations – Perspective transform.mp4 62.6 MB
6. Computer Vision Basics Part 3/7. Image Scaling – Pyramiding updown.mp4 60.3 MB
6. Computer Vision Basics Part 3/6. [Activity] Code to perform corner detection.mp4 59.9 MB
4. Computer Vision Basics Part 1/13. [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp4 55.1 MB
5. Computer Vision Basics Part 2/7. Region of interest masking.mp4 54.4 MB
4. Computer Vision Basics Part 1/5. [Activity] Detect lane lines in gray scale image.mp4 49.5 MB
10. Deep Learning and Tensorflow Part 1/4. ReLU Activation, and Preventing Overfitting with Dropout Regularlization.mp4 45.7 MB
9. Artificial Neural Networks/3. Activation Functions.mp4 44.6 MB
6. Computer Vision Basics Part 3/2. [Activity] Find a truck in an image manually!.mp4 44.5 MB
6. Computer Vision Basics Part 3/13. Feature Extraction - SIFT, SURF, FAST and ORB.mp4 44.5 MB
11. Deep Learning and Tensorflow Part 2/2. Implementing CNN's in Keras.mp4 44.4 MB
6. Computer Vision Basics Part 3/4. [Activity] Project Solution Find a Truck Using Template Matching.mp4 43.5 MB
10. Deep Learning and Tensorflow Part 1/5. [Activity] Improving our Classifier with Dropout Regularization.mp4 43.4 MB
7. Machine Learning Part 1/4. [Activity] Linear Regression in Action.mp4 43.2 MB
6. Computer Vision Basics Part 3/10. [Activity] Code to obtain color histogram.mp4 42.3 MB
8. Machine Learning Part 2/3. Support Vector Machines (SVM) and Support Vector Classifiers (SVC).mp4 42.1 MB
7. Machine Learning Part 1/3. Linear Regression.mp4 37.7 MB
4. Computer Vision Basics Part 1/6. [Activity] Detect lane lines in colored image.mp4 35.5 MB
6. Computer Vision Basics Part 3/14. [Activity] FASTORB Feature Extraction in OpenCV.mp4 35.5 MB
6. Computer Vision Basics Part 3/9. Histogram of colors.mp4 34.5 MB
3. Python Crash Course [Optional]/2. Python Basics Tuples and Dictionaries.mp4 32.3 MB
6. Computer Vision Basics Part 3/8. [Activity] Code to perform Image pyramiding.mp4 30.6 MB
3. Python Crash Course [Optional]/3. Python Basics Functions and Boolean Operations.mp4 28.5 MB
12. Wrapping Up/1. Bonus Lecture Keep Learning with Sundog Education.mp4 23.3 MB
1. Environment Setup and Installation/4. Udemy 101 Getting the Most From This Course.mp4 20.7 MB
3. Python Crash Course [Optional]/4. Python Basics Looping and an Exercise.mp4 20.0 MB
2. Introduction to Self-Driving Cars/2. Course Overview and Learning Outcomes.mp4 15.2 MB
7. Machine Learning Part 1/5. Logistic Regression.mp4 11.9 MB
11. Deep Learning and Tensorflow Part 2/5. Max Pooling.mp4 8.9 MB
9. Artificial Neural Networks/10. Example 1 - Build Multi-layer perceptron for binary classification.vtt 56.2 kB
11. Deep Learning and Tensorflow Part 2/8. [Activity] Build a CNN to Classify Traffic Siigns - part 2.vtt 26.5 kB
3. Python Crash Course [Optional]/7. Introduction to Seaborn.vtt 26.1 kB
8. Machine Learning Part 2/6. [Activity] Detecting Cars Using SVM - Part #2.vtt 24.4 kB
4. Computer Vision Basics Part 1/9. [Activity] Convert RGB to HSV color spaces and mergesplit channels.vtt 21.3 kB
10. Deep Learning and Tensorflow Part 1/3. [Activity] Building a Logistic Classifier with Deep Learning and Keras.vtt 21.2 kB
9. Artificial Neural Networks/4. ANN Training and dataset split.vtt 20.9 kB
7. Machine Learning Part 1/8. [Activity] Decision Trees In Action.vtt 20.2 kB
9. Artificial Neural Networks/2. Single Neuron Perceptron Model.vtt 19.6 kB
3. Python Crash Course [Optional]/6. Introduction to MatPlotLib.vtt 19.5 kB
5. Computer Vision Basics Part 2/9. Hough transform theory.vtt 19.5 kB
6. Computer Vision Basics Part 3/11. Histogram of Oriented Gradients (HOG).vtt 18.2 kB
9. Artificial Neural Networks/1. Introduction What are Artificial Neural Networks and how do they learn.vtt 18.1 kB
3. Python Crash Course [Optional]/5. Introduction to Pandas.vtt 17.7 kB
11. Deep Learning and Tensorflow Part 2/7. [Activity] Build a CNN to Classify Traffic Signs.vtt 17.7 kB
2. Introduction to Self-Driving Cars/1. A Brief History of Autonomous Vehicles.vtt 17.6 kB
5. Computer Vision Basics Part 2/11. Project Solution Hough transform to detect lane lines in an image.vtt 17.2 kB
9. Artificial Neural Networks/8. Code to Train a perceptron for binary classification.vtt 16.3 kB
10. Deep Learning and Tensorflow Part 1/2. Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding..vtt 16.0 kB
3. Python Crash Course [Optional]/1. Python Basics Whitespace, Imports, and Lists.vtt 16.0 kB
7. Machine Learning Part 1/2. Evaluating Machine Learning Systems with Cross-Validation.vtt 16.0 kB
4. Computer Vision Basics Part 1/2. Humans vs. Computers Vision system.vtt 15.7 kB
9. Artificial Neural Networks/6. Code to build a perceptron for binary classification.vtt 15.4 kB
4. Computer Vision Basics Part 1/12. Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).vtt 15.1 kB
7. Machine Learning Part 1/6. [Activity] Logistic Regression In Action.vtt 15.0 kB
10. Deep Learning and Tensorflow Part 1/1. Intro to Deep Learning and Tensorflow.vtt 15.0 kB
11. Deep Learning and Tensorflow Part 2/6. [Activity] Improving our CNN's Topology and with Max Pooling.vtt 14.9 kB
8. Machine Learning Part 2/5. Project Solution Detecting Cars Using SVM - Part #1.vtt 14.6 kB
4. Computer Vision Basics Part 1/8. Color Spaces.vtt 14.5 kB
8. Machine Learning Part 2/1. Bayes Theorem and Naive Bayes.vtt 14.4 kB
7. Machine Learning Part 1/1. What is Machine Learning.vtt 14.3 kB
7. Machine Learning Part 1/7. Decision Trees and Random Forests.vtt 14.0 kB
5. Computer Vision Basics Part 2/2. [Activity] Code to perform rotation, translation and resizing.vtt 13.9 kB
8. Machine Learning Part 2/2. [Activity] Naive Bayes in Action.vtt 13.8 kB
4. Computer Vision Basics Part 1/1. What is computer vision and why is it important.vtt 13.3 kB
9. Artificial Neural Networks/11. Example 2 - Build Multi-layer perceptron for binary classification.vtt 13.3 kB
4. Computer Vision Basics Part 1/4. [Activity] View colored image and convert RGB to Gray.vtt 13.3 kB
8. Machine Learning Part 2/7. [Activity] Project Solution Detecting Cars Using SVM - Part #3.vtt 12.4 kB
4. Computer Vision Basics Part 1/3. what is an image and how is it digitally stored.vtt 12.3 kB
8. Machine Learning Part 2/4. [Activity] Support Vector Classifiers in Action.vtt 12.0 kB
9. Artificial Neural Networks/7. Backpropagation Training.vtt 11.5 kB
11. Deep Learning and Tensorflow Part 2/3. [Activity] Classifying Images with a Simple CNN, Part 1.vtt 11.5 kB
11. Deep Learning and Tensorflow Part 2/4. [Activity] Classifying Images with a Simple CNN, Part 2.vtt 11.5 kB
4. Computer Vision Basics Part 1/10. Convolutions - Sharpening and Blurring.vtt 11.1 kB
5. Computer Vision Basics Part 2/8. [Activity] Code to define the region of interest.vtt 11.0 kB
5. Computer Vision Basics Part 2/10. [Activity] Hough transform – practical example in python.vtt 10.6 kB
9. Artificial Neural Networks/9. Two and Multi-layer Perceptron ANN.vtt 10.4 kB
5. Computer Vision Basics Part 2/5. Image cropping dilation and erosion.vtt 10.1 kB
11. Deep Learning and Tensorflow Part 2/1. Convolutional Neural Networks (CNN's).vtt 10.0 kB
8. Machine Learning Part 2/3. Support Vector Machines (SVM) and Support Vector Classifiers (SVC).vtt 9.8 kB
5. Computer Vision Basics Part 2/6. [Activity] Code to perform Image cropping dilation and erosion.vtt 9.6 kB
10. Deep Learning and Tensorflow Part 1/4. ReLU Activation, and Preventing Overfitting with Dropout Regularlization.vtt 9.6 kB
9. Artificial Neural Networks/5. Practical Example - Vehicle Speed Determination.vtt 9.4 kB
5. Computer Vision Basics Part 2/4. [Activity] Perform non-affine image transformation on a traffic sign image.vtt 9.3 kB
6. Computer Vision Basics Part 3/3. Template Matching - Find a Truck.vtt 9.1 kB
7. Machine Learning Part 1/4. [Activity] Linear Regression in Action.vtt 9.1 kB
11. Deep Learning and Tensorflow Part 2/2. Implementing CNN's in Keras.vtt 9.0 kB
4. Computer Vision Basics Part 1/11. [Activity] Convolutions - Sharpening and Blurring.vtt 9.0 kB
3. Python Crash Course [Optional]/2. Python Basics Tuples and Dictionaries.vtt 8.9 kB
7. Machine Learning Part 1/3. Linear Regression.vtt 8.9 kB
1. Environment Setup and Installation/3. Test your Environment with Real-Time Edge Detection in a Jupyter Notebook.vtt 8.8 kB
5. Computer Vision Basics Part 2/1. Image Transformation - Rotations, Translation and Resizing.vtt 8.7 kB
6. Computer Vision Basics Part 3/5. Corner detection – Harris.vtt 8.5 kB
4. Computer Vision Basics Part 1/14. [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam.vtt 8.4 kB
3. Python Crash Course [Optional]/3. Python Basics Functions and Boolean Operations.vtt 8.3 kB
6. Computer Vision Basics Part 3/1. Image Features and their importance for object detection.vtt 7.9 kB
3. Python Crash Course [Optional]/4. Python Basics Looping and an Exercise.vtt 7.4 kB
4. Computer Vision Basics Part 1/5. [Activity] Detect lane lines in gray scale image.vtt 7.4 kB
6. Computer Vision Basics Part 3/6. [Activity] Code to perform corner detection.vtt 7.4 kB
1. Environment Setup and Installation/2. Install Anaconda, OpenCV, Tensorflow, and the Course Materials.vtt 7.2 kB
5. Computer Vision Basics Part 2/7. Region of interest masking.vtt 7.1 kB
5. Computer Vision Basics Part 2/3. Image Transformations – Perspective transform.vtt 7.1 kB
4. Computer Vision Basics Part 1/13. [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).vtt 6.9 kB
9. Artificial Neural Networks/3. Activation Functions.vtt 6.5 kB
10. Deep Learning and Tensorflow Part 1/5. [Activity] Improving our Classifier with Dropout Regularization.vtt 6.4 kB
6. Computer Vision Basics Part 3/12. [Activity] Code to perform HOG Feature extraction.vtt 6.3 kB
4. Computer Vision Basics Part 1/7. What are the challenges of color selection technique.vtt 5.5 kB
4. Computer Vision Basics Part 1/6. [Activity] Detect lane lines in colored image.vtt 5.4 kB
6. Computer Vision Basics Part 3/7. Image Scaling – Pyramiding updown.vtt 5.3 kB
6. Computer Vision Basics Part 3/10. [Activity] Code to obtain color histogram.vtt 5.3 kB
6. Computer Vision Basics Part 3/4. [Activity] Project Solution Find a Truck Using Template Matching.vtt 5.1 kB
2. Introduction to Self-Driving Cars/2. Course Overview and Learning Outcomes.vtt 5.1 kB
6. Computer Vision Basics Part 3/2. [Activity] Find a truck in an image manually!.vtt 4.9 kB
7. Machine Learning Part 1/5. Logistic Regression.vtt 4.7 kB
1. Environment Setup and Installation/1. Introduction.vtt 4.2 kB
6. Computer Vision Basics Part 3/13. Feature Extraction - SIFT, SURF, FAST and ORB.vtt 4.1 kB
6. Computer Vision Basics Part 3/14. [Activity] FASTORB Feature Extraction in OpenCV.vtt 4.0 kB
11. Deep Learning and Tensorflow Part 2/5. Max Pooling.vtt 4.0 kB
1. Environment Setup and Installation/4. Udemy 101 Getting the Most From This Course.vtt 3.5 kB
6. Computer Vision Basics Part 3/8. [Activity] Code to perform Image pyramiding.vtt 3.3 kB
6. Computer Vision Basics Part 3/9. Histogram of colors.vtt 3.2 kB
12. Wrapping Up/1. Bonus Lecture Keep Learning with Sundog Education.vtt 1.8 kB
0. Websites you may like/[FCS Forum].url 133 Bytes
0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
0. Websites you may like/[CourseClub.ME].url 122 Bytes
1. Environment Setup and Installation/2.1 Course materials page.html 102 Bytes

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