Udemy - Tensorflow Tutorial: Hands-on AI development with Tensorflow

Udemy - Tensorflow Tutorial: Hands-on AI development with Tensorflow
     3. Cats vs Dogs.mp4 -


     TutsNode.com.txt -


     3. Cats vs Dogs.srt -


     1. Fashion Clothing Recognition.srt -


     6. Digit Classification.srt -


     4. Backpropagation.srt -


     8. Facial Recognition using PCA.srt -


     6. Logistic Regression - Implementation.srt -


     3. Linear Regression - Theory.srt -


     4. Linear Regression - Implementation.srt -


     2. Convolution in CNN (part1).srt -


     1. Decision Trees - Theory.srt -


     3. Convolution in CNN (part2).srt -


     3. Graphs.srt -


     1. K-Means Algorithm (Part 1).srt -


     3. Complex Decision Boundaries.srt -


     4. Layers of CNN.srt -


     2. K-Means Algorithm (Part 2).srt -


     2. CIFAR 10 and CNN.srt -


     4. K-Means ++.srt -


     7. Principal Component Analysis.srt -


     1. What is TensorFlow 2 Preview.srt -


     6. Famous CNN Architectures.srt -


     1. Introduction.srt -


     2. Gates and Forward Propagation.srt -


     5. Digit Classification.srt -


     6. Intro to Machine Learning.srt -


     5. Logistic Regression - Theory.srt -


     2. Basics of TensorFlow.srt -


     1. Introduction.srt -


     4. Automatic Differentiation.srt -


     4. Action Recognition.srt -


     2. Decision Trees - Implementation.srt -


     7. Overfitting and Regularization.srt -


     3. Centroid Initialization.srt -


     5. Keras and TensorFlow.srt -


     5. Gradient Descent Type and Softmax.srt -


     [TGx]Downloaded from torrentgalaxy.to .txt -


     0 -


     1. Fashion Clothing Recognition.mp4 -


     6. K-Means Implementation.srt -


     8. Model Evaluation - Theory.srt -


     5. Number of Clusters.srt -


     9. Model Evaluation - Implementation.srt -


     7. Types of Supervised Learning.srt -


     1 -


     8. Facial Recognition using PCA.mp4 -


     2 -


     4. Backpropagation.mp4 -


     3 -


     6. Digit Classification.mp4 -


     4 -


     3. Linear Regression - Theory.mp4 -


     5 -


     1. Decision Trees - Theory.mp4 -


     6 -


     2. Convolution in CNN (part1).mp4 -


     7 -


     3. Convolution in CNN (part2).mp4 -


     8 -


     6. Logistic Regression - Implementation.mp4 -


     9 -


     4. Layers of CNN.mp4 -


     10 -


     6. Intro to Machine Learning.mp4 -


     11 -


     3. Graphs.mp4 -


     12 -


     3. Complex Decision Boundaries.mp4 -


     13 -


     7. Principal Component Analysis.mp4 -


     14 -


     1. Introduction.mp4 -


     15 -


     5. Logistic Regression - Theory.mp4 -


     16 -


     2. Gates and Forward Propagation.mp4 -


     17 -


     4. Linear Regression - Implementation.mp4 -


     18 -


     2. K-Means Algorithm (Part 2).mp4 -


     19 -


     1. Introduction.mp4 -


     20 -


     1. K-Means Algorithm (Part 1).mp4 -


     21 -


     6. Famous CNN Architectures.mp4 -


     22 -


     5. Digit Classification.mp4 -


     23 -


     4. K-Means ++.mp4 -


     24 -


     1. What is TensorFlow 2 Preview.mp4 -


     25 -


     2. CIFAR 10 and CNN.mp4 -


     26 -


     4. Action Recognition.mp4 -


     27 -


     4. Automatic Differentiation.mp4 -


     28 -


     7. Overfitting and Regularization.mp4 -


     29 -


     2. Basics of TensorFlow.mp4 -


     30 -


     5. Keras and TensorFlow.mp4 -


     31 -


     3. Centroid Initialization.mp4 -


     32 -


     5. Gradient Descent Type and Softmax.mp4 -


     33 -


     2. Decision Trees - Implementation.mp4 -


     34 -


     6. K-Means Implementation.mp4 -


     35 -


     8. Model Evaluation - Theory.mp4 -


     36 -


     9. Model Evaluation - Implementation.mp4 -


     37 -


     5. Number of Clusters.mp4 -


     38 -


     7. Types of Supervised Learning.mp4 -

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