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

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视频 2021-2-9 02:21 2024-6-14 21:38 359 7.36 GB 92
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FreeCourseSite com Udemy - Autonomous Cars Deep Learning and Computer Vision in Python

文件列表
  1. 1. Environment Setup and Installation/1. Introduction.mp474.83MB
  2. 1. Environment Setup and Installation/2. Install Anaconda, OpenCV, Tensorflow, and the Course Materials.mp461.53MB
  3. 1. Environment Setup and Installation/3. Test your Environment with Real-Time Edge Detection in a Jupyter Notebook.mp461.34MB
  4. 1. Environment Setup and Installation/4. Udemy 101 Getting the Most From This Course.mp419.73MB
  5. 10. Deep Learning and Tensorflow Part 1/1. Intro to Deep Learning and Tensorflow.mp474.52MB
  6. 10. Deep Learning and Tensorflow Part 1/2. Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding..mp463.86MB
  7. 10. Deep Learning and Tensorflow Part 1/3. [Activity] Building a Logistic Classifier with Deep Learning and Keras.mp4134.56MB
  8. 10. Deep Learning and Tensorflow Part 1/4. ReLU Activation, and Preventing Overfitting with Dropout Regularlization.mp443.57MB
  9. 10. Deep Learning and Tensorflow Part 1/5. [Activity] Improving our Classifier with Dropout Regularization.mp441.38MB
  10. 11. Deep Learning and Tensorflow Part 2/1. Convolutional Neural Networks (CNN's).mp470.9MB
  11. 11. Deep Learning and Tensorflow Part 2/2. Implementing CNN's in Keras.mp442.33MB
  12. 11. Deep Learning and Tensorflow Part 2/3. [Activity] Classifying Images with a Simple CNN, Part 1.mp483.58MB
  13. 11. Deep Learning and Tensorflow Part 2/4. [Activity] Classifying Images with a Simple CNN, Part 2.mp467.15MB
  14. 11. Deep Learning and Tensorflow Part 2/5. Max Pooling.mp48.49MB
  15. 11. Deep Learning and Tensorflow Part 2/6. [Activity] Improving our CNN's Topology and with Max Pooling.mp4102.24MB
  16. 11. Deep Learning and Tensorflow Part 2/7. [Activity] Build a CNN to Classify Traffic Signs.mp4150.58MB
  17. 11. Deep Learning and Tensorflow Part 2/8. [Activity] Build a CNN to Classify Traffic Siigns - part 2.mp4175.33MB
  18. 12. Wrapping Up/1. Bonus Lecture Keep Learning with Sundog Education.mp422.18MB
  19. 2. Introduction to Self-Driving Cars/1. A Brief History of Autonomous Vehicles.mp4145.92MB
  20. 2. Introduction to Self-Driving Cars/2. Course Overview and Learning Outcomes.mp414.53MB
  21. 3. Python Crash Course [Optional]/1. Python Basics Whitespace, Imports, and Lists.mp468.22MB
  22. 3. Python Crash Course [Optional]/2. Python Basics Tuples and Dictionaries.mp430.8MB
  23. 3. Python Crash Course [Optional]/3. Python Basics Functions and Boolean Operations.mp427.21MB
  24. 3. Python Crash Course [Optional]/4. Python Basics Looping and an Exercise.mp419.1MB
  25. 3. Python Crash Course [Optional]/5. Introduction to Pandas.mp485.99MB
  26. 3. Python Crash Course [Optional]/6. Introduction to MatPlotLib.mp485.11MB
  27. 3. Python Crash Course [Optional]/7. Introduction to Seaborn.mp4146.72MB
  28. 4. Computer Vision Basics Part 1/1. What is computer vision and why is it important.mp4118.76MB
  29. 4. Computer Vision Basics Part 1/10. Convolutions - Sharpening and Blurring.mp484.09MB
  30. 4. Computer Vision Basics Part 1/11. [Activity] Convolutions - Sharpening and Blurring.mp466.06MB
  31. 4. Computer Vision Basics Part 1/12. Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp498.76MB
  32. 4. Computer Vision Basics Part 1/13. [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny).mp452.56MB
  33. 4. Computer Vision Basics Part 1/14. [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam.mp467.55MB
  34. 4. Computer Vision Basics Part 1/2. Humans vs. Computers Vision system.mp4135.32MB
  35. 4. Computer Vision Basics Part 1/3. what is an image and how is it digitally stored.mp498.55MB
  36. 4. Computer Vision Basics Part 1/4. [Activity] View colored image and convert RGB to Gray.mp486.13MB
  37. 4. Computer Vision Basics Part 1/5. [Activity] Detect lane lines in gray scale image.mp447.25MB
  38. 4. Computer Vision Basics Part 1/6. [Activity] Detect lane lines in colored image.mp433.85MB
  39. 4. Computer Vision Basics Part 1/7. What are the challenges of color selection technique.mp462.49MB
  40. 4. Computer Vision Basics Part 1/8. Color Spaces.mp4113.66MB
  41. 4. Computer Vision Basics Part 1/9. [Activity] Convert RGB to HSV color spaces and mergesplit channels.mp4166.92MB
  42. 5. Computer Vision Basics Part 2/1. Image Transformation - Rotations, Translation and Resizing.mp475.5MB
  43. 5. Computer Vision Basics Part 2/10. [Activity] Hough transform – practical example in python.mp475.83MB
  44. 5. Computer Vision Basics Part 2/11. Project Solution Hough transform to detect lane lines in an image.mp4117.05MB
  45. 5. Computer Vision Basics Part 2/2. [Activity] Code to perform rotation, translation and resizing.mp4102.05MB
  46. 5. Computer Vision Basics Part 2/3. Image Transformations – Perspective transform.mp459.66MB
  47. 5. Computer Vision Basics Part 2/4. [Activity] Perform non-affine image transformation on a traffic sign image.mp468.65MB
  48. 5. Computer Vision Basics Part 2/5. Image cropping dilation and erosion.mp487.77MB
  49. 5. Computer Vision Basics Part 2/6. [Activity] Code to perform Image cropping dilation and erosion.mp476.93MB
  50. 5. Computer Vision Basics Part 2/7. Region of interest masking.mp451.92MB
  51. 5. Computer Vision Basics Part 2/8. [Activity] Code to define the region of interest.mp480.31MB
  52. 5. Computer Vision Basics Part 2/9. Hough transform theory.mp4141.5MB
  53. 6. Computer Vision Basics Part 3/1. Image Features and their importance for object detection.mp479MB
  54. 6. Computer Vision Basics Part 3/10. [Activity] Code to obtain color histogram.mp440.35MB
  55. 6. Computer Vision Basics Part 3/11. Histogram of Oriented Gradients (HOG).mp4169.49MB
  56. 6. Computer Vision Basics Part 3/12. [Activity] Code to perform HOG Feature extraction.mp461.04MB
  57. 6. Computer Vision Basics Part 3/13. Feature Extraction - SIFT, SURF, FAST and ORB.mp442.43MB
  58. 6. Computer Vision Basics Part 3/14. [Activity] FASTORB Feature Extraction in OpenCV.mp433.83MB
  59. 6. Computer Vision Basics Part 3/2. [Activity] Find a truck in an image manually!.mp442.47MB
  60. 6. Computer Vision Basics Part 3/3. Template Matching - Find a Truck.mp490.26MB
  61. 6. Computer Vision Basics Part 3/4. [Activity] Project Solution Find a Truck Using Template Matching.mp441.52MB
  62. 6. Computer Vision Basics Part 3/5. Corner detection – Harris.mp476.91MB
  63. 6. Computer Vision Basics Part 3/6. [Activity] Code to perform corner detection.mp457.08MB
  64. 6. Computer Vision Basics Part 3/7. Image Scaling – Pyramiding updown.mp457.54MB
  65. 6. Computer Vision Basics Part 3/8. [Activity] Code to perform Image pyramiding.mp429.17MB
  66. 6. Computer Vision Basics Part 3/9. Histogram of colors.mp432.91MB
  67. 7. Machine Learning Part 1/1. What is Machine Learning.mp496.3MB
  68. 7. Machine Learning Part 1/2. Evaluating Machine Learning Systems with Cross-Validation.mp465.98MB
  69. 7. Machine Learning Part 1/3. Linear Regression.mp435.93MB
  70. 7. Machine Learning Part 1/4. [Activity] Linear Regression in Action.mp441.21MB
  71. 7. Machine Learning Part 1/5. Logistic Regression.mp411.37MB
  72. 7. Machine Learning Part 1/6. [Activity] Logistic Regression In Action.mp493.02MB
  73. 7. Machine Learning Part 1/7. Decision Trees and Random Forests.mp461.53MB
  74. 7. Machine Learning Part 1/8. [Activity] Decision Trees In Action.mp4103.65MB
  75. 8. Machine Learning Part 2/1. Bayes Theorem and Naive Bayes.mp476.03MB
  76. 8. Machine Learning Part 2/2. [Activity] Naive Bayes in Action.mp478.78MB
  77. 8. Machine Learning Part 2/3. Support Vector Machines (SVM) and Support Vector Classifiers (SVC).mp440.18MB
  78. 8. Machine Learning Part 2/4. [Activity] Support Vector Classifiers in Action.mp474.35MB
  79. 8. Machine Learning Part 2/5. Project Solution Detecting Cars Using SVM - Part #1.mp4119.72MB
  80. 8. Machine Learning Part 2/6. [Activity] Detecting Cars Using SVM - Part #2.mp4204.08MB
  81. 8. Machine Learning Part 2/7. [Activity] Project Solution Detecting Cars Using SVM - Part #3.mp4116.83MB
  82. 9. Artificial Neural Networks/1. Introduction What are Artificial Neural Networks and how do they learn.mp4127.77MB
  83. 9. Artificial Neural Networks/10. Example 1 - Build Multi-layer perceptron for binary classification.mp4384.19MB
  84. 9. Artificial Neural Networks/11. Example 2 - Build Multi-layer perceptron for binary classification.mp4102.28MB
  85. 9. Artificial Neural Networks/2. Single Neuron Perceptron Model.mp4119.67MB
  86. 9. Artificial Neural Networks/3. Activation Functions.mp442.55MB
  87. 9. Artificial Neural Networks/4. ANN Training and dataset split.mp4151.3MB
  88. 9. Artificial Neural Networks/5. Practical Example - Vehicle Speed Determination.mp467.5MB
  89. 9. Artificial Neural Networks/6. Code to build a perceptron for binary classification.mp4111.6MB
  90. 9. Artificial Neural Networks/7. Backpropagation Training.mp484.25MB
  91. 9. Artificial Neural Networks/8. Code to Train a perceptron for binary classification.mp4110.23MB
  92. 9. Artificial Neural Networks/9. Two and Multi-layer Perceptron ANN.mp471.05MB
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