本站已收录 番号和无损神作磁力链接/BT种子 

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

种子简介

种子名称: [FreeCourseSite.com] Udemy - Autonomous Cars Deep Learning and Computer Vision in Python
文件类型: 视频
文件数目: 92个文件
文件大小: 7.36 GB
收录时间: 2021-2-9 02:21
已经下载: 3
资源热度: 409
最近下载: 2024-12-23 23:58

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:a13b706a40564a0b20e1e5b225095f2d283c44df&dn=[FreeCourseSite.com] Udemy - Autonomous Cars Deep Learning and Computer Vision in Python 复制链接到迅雷、QQ旋风进行下载,或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

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