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： 2021-8-8 06:44
已经下载： 3次
资源热度： 236
最近下载： 2024-11-28 02:28

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:268a4c130da593e4182ed4d1bfce981b0e56aa1c&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