[CourseRecap.Com] - Tensorflow Tutorial Hands-on AI development with Tensorflow

磁力链接/BT种子名称

[CourseRecap.Com] - Tensorflow Tutorial Hands-on AI development with Tensorflow

磁力链接/BT种子简介

种子哈希：df1c08ba7a08d38e9f5666da2d567c9c805e8910
文件大小： 3.96G
已经下载：52次
下载速度：极快
收录时间：2021-06-01
最近下载：2022-05-06

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文件列表

  • 6. Live Projects/3. Cats vs Dogs.mp4 303.6 MB
  • 6. Live Projects/1. Fashion Clothing Recognition.mp4 164.3 MB
  • 5. Section 5/8. Facial Recognition using PCA.mp4 156.4 MB
  • 3. Section 3/4. Backpropagation.mp4 154.3 MB
  • 3. Section 3/6. Digit Classification.mp4 151.4 MB
  • 2. Section 2/3. Linear Regression - Theory.mp4 151.1 MB
  • 2. Section 2/1. Decision Trees - Theory.mp4 145.5 MB
  • 4. Section 4/2. Convolution in CNN (part1).mp4 143.9 MB
  • 4. Section 4/3. Convolution in CNN (part2).mp4 138.1 MB
  • 2. Section 2/6. Logistic Regression - Implementation.mp4 123.0 MB
  • 4. Section 4/4. Layers of CNN.mp4 119.4 MB
  • 1. Section 1/6. Intro to Machine Learning.mp4 117.9 MB
  • 1. Section 1/3. Graphs.mp4 117.7 MB
  • 3. Section 3/3. Complex Decision Boundaries.mp4 117.1 MB
  • 5. Section 5/7. Principal Component Analysis.mp4 115.2 MB
  • 4. Section 4/1. Introduction.mp4 114.4 MB
  • 2. Section 2/5. Logistic Regression - Theory.mp4 112.8 MB
  • 3. Section 3/2. Gates and Forward Propagation.mp4 109.3 MB
  • 2. Section 2/4. Linear Regression - Implementation.mp4 109.1 MB
  • 5. Section 5/2. K-Means Algorithm (Part 2).mp4 108.2 MB
  • 3. Section 3/1. Introduction.mp4 105.2 MB
  • 5. Section 5/1. K-Means Algorithm (Part 1).mp4 104.8 MB
  • 4. Section 4/6. Famous CNN Architectures.mp4 101.1 MB
  • 4. Section 4/5. Digit Classification.mp4 98.5 MB
  • 5. Section 5/4. K-Means ++.mp4 94.7 MB
  • 1. Section 1/1. What is TensorFlow 2 Preview.mp4 91.1 MB
  • 6. Live Projects/2. CIFAR 10 and CNN.mp4 87.7 MB
  • 6. Live Projects/4. Action Recognition.mp4 86.1 MB
  • 1. Section 1/4. Automatic Differentiation.mp4 81.3 MB
  • 2. Section 2/7. Overfitting and Regularization.mp4 79.8 MB
  • 1. Section 1/2. Basics of TensorFlow.mp4 78.0 MB
  • 1. Section 1/5. Keras and TensorFlow.mp4 70.2 MB
  • 5. Section 5/3. Centroid Initialization.mp4 69.5 MB
  • 3. Section 3/5. Gradient Descent Type and Softmax.mp4 65.9 MB
  • 2. Section 2/2. Decision Trees - Implementation.mp4 63.9 MB
  • 5. Section 5/6. K-Means Implementation.mp4 53.2 MB
  • 2. Section 2/8. Model Evaluation - Theory.mp4 48.5 MB
  • 2. Section 2/9. Model Evaluation - Implementation.mp4 34.6 MB
  • 5. Section 5/5. Number of Clusters.mp4 33.3 MB
  • 1. Section 1/7. Types of Supervised Learning.mp4 28.9 MB
  • 6. Live Projects/3. Cats vs Dogs.srt 40.8 kB
  • 6. Live Projects/1. Fashion Clothing Recognition.srt 22.7 kB
  • 3. Section 3/6. Digit Classification.srt 21.6 kB
  • 3. Section 3/4. Backpropagation.srt 21.2 kB
  • 5. Section 5/8. Facial Recognition using PCA.srt 19.9 kB
  • 2. Section 2/6. Logistic Regression - Implementation.srt 18.4 kB
  • 2. Section 2/3. Linear Regression - Theory.srt 17.8 kB
  • 2. Section 2/4. Linear Regression - Implementation.srt 17.4 kB
  • 4. Section 4/2. Convolution in CNN (part1).srt 16.9 kB
  • 2. Section 2/1. Decision Trees - Theory.srt 15.6 kB
  • 4. Section 4/3. Convolution in CNN (part2).srt 15.3 kB
  • 1. Section 1/3. Graphs.srt 15.3 kB
  • 5. Section 5/1. K-Means Algorithm (Part 1).srt 14.6 kB
  • 3. Section 3/3. Complex Decision Boundaries.srt 13.9 kB
  • 4. Section 4/4. Layers of CNN.srt 13.3 kB
  • 5. Section 5/2. K-Means Algorithm (Part 2).srt 13.2 kB
  • 6. Live Projects/2. CIFAR 10 and CNN.srt 13.0 kB
  • 5. Section 5/4. K-Means ++.srt 12.9 kB
  • 5. Section 5/7. Principal Component Analysis.srt 12.9 kB
  • 1. Section 1/1. What is TensorFlow 2 Preview.srt 12.9 kB
  • 4. Section 4/6. Famous CNN Architectures.srt 12.5 kB
  • 3. Section 3/1. Introduction.srt 12.3 kB
  • 3. Section 3/2. Gates and Forward Propagation.srt 12.2 kB
  • 4. Section 4/5. Digit Classification.srt 12.1 kB
  • 1. Section 1/6. Intro to Machine Learning.srt 12.1 kB
  • 2. Section 2/5. Logistic Regression - Theory.srt 12.0 kB
  • 1. Section 1/2. Basics of TensorFlow.srt 11.9 kB
  • 4. Section 4/1. Introduction.srt 11.8 kB
  • 1. Section 1/4. Automatic Differentiation.srt 11.6 kB
  • 6. Live Projects/4. Action Recognition.srt 10.6 kB
  • 2. Section 2/2. Decision Trees - Implementation.srt 10.3 kB
  • 2. Section 2/7. Overfitting and Regularization.srt 9.2 kB
  • 5. Section 5/3. Centroid Initialization.srt 8.9 kB
  • 1. Section 1/5. Keras and TensorFlow.srt 8.0 kB
  • 3. Section 3/5. Gradient Descent Type and Softmax.srt 7.9 kB
  • 5. Section 5/6. K-Means Implementation.srt 7.6 kB
  • 2. Section 2/8. Model Evaluation - Theory.srt 5.4 kB
  • 5. Section 5/5. Number of Clusters.srt 4.7 kB
  • 2. Section 2/9. Model Evaluation - Implementation.srt 4.1 kB
  • 1. Section 1/7. Types of Supervised Learning.srt 3.9 kB
  • READ_ME.txt 503 Bytes
  • 1. Section 1/READ_ME.txt 503 Bytes
  • 1. Section 1/CourseRecap-Click For More Courses!!.url 50 Bytes
  • CourseRecap-Click For More Courses!!.url 50 Bytes

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