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1. From TensorFlow 1.x to TensorFlow 2.0.mp4
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1. Plan of Attack.mp4
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1. Project Setup & Data Preprocessing.mp4
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1. Project Setup.mp4
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1. Project Setup.srt
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1. SPECIAL COVID-19 BONUS.html
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1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..mp4
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146.3 MB
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1. Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit..srt
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1. What is Reinforcement Learning.mp4
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1. What is Transfer Learning.mp4
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1. What is Transfer Learning.srt
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1. What is the Distributed Training.mp4
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1. What is the Distributed Training.srt
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1. What is the TensorFlow Lite.mp4
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1. What is the TensorFlow Lite.srt
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1. What is the TensorFlow Serving.mp4
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1. What is the TensorFlow Serving.srt
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1.1 Flask API.zip
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372.4 KB
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1.1 Google Colab ANN.html
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102.4 B
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1.1 Google Colab CNN.html
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102.4 B
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1.1 Google Colab Deep-Q Trading Bot.html
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102.4 B
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1.1 Google Colab RNN.html
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102.4 B
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1.1 Google Colab TFT.html
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102.4 B
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1.1 Google Colab TensorFlow 1.x to TensorFlow 2.0.html
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102.4 B
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1.1 pollution_small.csv
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1.2 Google Colab TFDV.html
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102.4 B
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1.2 pollution_small.csv
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10. Defining the model.mp4
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10. Defining the model.srt
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10. Sending the POST request to a specific model.mp4
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10. Sending the POST request to a specific model.srt
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10. Transfer Learning.mp4
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10. Transfer Learning.srt
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10. What's next.html
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11. Evaluating Transfer Learning results.mp4
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11. Evaluating Transfer Learning results.srt
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11. Training loop - Step 1.mp4
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12. Fine Tuning model definition.mp4
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12. Fine Tuning model definition.srt
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12. Training loop - Step 2.mp4
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12. Training loop - Step 2.srt
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13. Compiling the Fine Tuning model.mp4
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13. Compiling the Fine Tuning model.srt
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14. Fine Tuning.mp4
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14. Fine Tuning.srt
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15. Evaluating Fine Tuning results.mp4
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9 MB
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15. Evaluating Fine Tuning results.srt
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16. Transfer Learning quiz.html
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102.4 B
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2. AI Trader - Step 1.mp4
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27.2 MB
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2. AI Trader - Step 1.srt
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2. Building the Convolutional Neural Network.mp4
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2. Building the Convolutional Neural Network.srt
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2. Building the Recurrent Neural Network.mp4
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40 MB
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2. Building the Recurrent Neural Network.srt
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2. Constants, Variables, Tensors.mp4
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2. Constants, Variables, Tensors.srt
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2. Course Curriculum & Colab Toolkit.html
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512 B
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2. Data Preprocessing.mp4
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61.8 MB
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2. Data Preprocessing.srt
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2. Importing project dependencies.mp4
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2. Importing project dependencies.srt
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2. Initial dataset preprocessing.mp4
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35 MB
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2. Initial dataset preprocessing.srt
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2. Loading the pollution dataset.mp4
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2. Loading the pollution dataset.srt
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2. Project Setup.mp4
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49.4 MB
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2. Project Setup.srt
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2. Project setup.mp4
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8 MB
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2. Project setup.srt
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2. TensorFlow Serving architecture.mp4
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2. TensorFlow Serving architecture.srt
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2. The Bellman Equation.mp4
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2. The Bellman Equation.srt
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2. The Neuron.mp4
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2. The Neuron.srt
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25 KB
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2. What are Convolutional Neural Networks.mp4
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107.9 MB
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2. What are Convolutional Neural Networks.srt
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2. What are Recurrent Neural Networks.mp4
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120.9 MB
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2. What are Recurrent Neural Networks.srt
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24.3 KB
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2. YOUR SPECIAL BONUS.html
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1.1 KB
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2.1 Google Colab Distributed Training.html
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HTML
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102.4 B
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2.1 Google Colab TensorFlow Lite.html
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102.4 B
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2.1 Google Colab Transfer Learning and Fine Tuning.html
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102.4 B
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3. AI Trader - Step 2.mp4
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11.9 MB
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3. AI Trader - Step 2.srt
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3. BONUS 10 advantages of TensorFlow.html
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614.4 B
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3. Building the Artificial Neural Network.mp4
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60.4 MB
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3. Building the Artificial Neural Network.srt
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3. Creating dataset Schema.mp4
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3. Creating dataset Schema.srt
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3. Dataset metadata.mp4
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3. Dataset metadata.srt
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3. Dataset preprocessing.mp4
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3. Dataset preprocessing.srt
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3. FREE LEARNING RESOURCES FOR YOU.html
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2.3 KB
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3. Loading a pre-trained model.mp4
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20.5 MB
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3. Loading a pre-trained model.srt
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3. Markov Decision Process (MDP).mp4
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94.3 MB
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3. Markov Decision Process (MDP).srt
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3. Operations with Tensors.mp4
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49.3 MB
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3. Operations with Tensors.srt
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3. Project setup.mp4
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25.5 MB
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3. Project setup.srt
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3. Step 1 - Convolution.mp4
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3. Step 1 - Convolution.srt
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3. The Activation Function.mp4
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3. The Activation Function.srt
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3. Training and Evaluating the Convolutional Neural Network.mp4
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3. Training and Evaluating the Convolutional Neural Network.srt
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11 KB
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3. Training and Evaluating the Recurrent Neural Network.mp4
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3. Training and Evaluating the Recurrent Neural Network.srt
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3. Vanishing Gradient.mp4
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111 MB
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3. Vanishing Gradient.srt
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3.1 Google Colab TensorFlow Serving.html
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102.4 B
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4. AI Trader - Step 3.mp4
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15.8 MB
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4. AI Trader - Step 3.srt
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3 KB
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4. BONUS Learning Path.html
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1.4 KB
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4. Building a model.mp4
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14.8 MB
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4. Building a model.srt
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4. Computing test set statistics.mp4
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4. Computing test set statistics.srt
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4. Convolutional Neural Networks Quiz.html
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102.4 B
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4. Dataset preprocessing.mp4
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4. Dataset preprocessing.srt
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4. Defining a non-distributed model (normal CNN model).mp4
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14 MB
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4. Defining a non-distributed model (normal CNN model).srt
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4. Defining the Flask application.mp4
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4. Defining the Flask application.srt
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4. How do Neural Networks Work.mp4
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81.8 MB
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4. How do Neural Networks Work.srt
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19 KB
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4. LSTMs.mp4
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136.4 MB
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4. LSTMs.srt
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4. Loading the MobileNet V2 model.mp4
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17.8 MB
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4. Loading the MobileNet V2 model.srt
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4. Preprocessing function.mp4
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4. Preprocessing function.srt
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4. Q-Learning Intuition.mp4
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79.1 MB
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4. Q-Learning Intuition.srt
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21.7 KB
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4. Recurrent Neural Network Quiz.html
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102.4 B
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4. Step 1 Bis - ReLU Layer.mp4
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53.4 MB
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4. Step 1 Bis - ReLU Layer.srt
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4. Strings.mp4
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4. Strings.srt
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4. Training the Artificial Neural Network.mp4
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48.5 MB
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4. Training the Artificial Neural Network.srt
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5. AI Trader - Step 4.mp4
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5. AI Trader - Step 4.srt
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5. Anomaly detection with TensorFlow Data Validation.mp4
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24 MB
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5. Anomaly detection with TensorFlow Data Validation.srt
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5. Creating classify function.mp4
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5. Creating classify function.srt
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5. Dataset preprocessing pipeline.mp4
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74 MB
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5. Dataset preprocessing pipeline.srt
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5. Defining, training and evaluating a model.mp4
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5. Defining, training and evaluating a model.srt
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5. Evaluating the Artificial Neural Network.mp4
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31.4 MB
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5. Evaluating the Artificial Neural Network.srt
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5. Freezing the pre-trained model.mp4
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5. Freezing the pre-trained model.srt
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5. HOMEWORK Convolutional Neural Networks.html
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512 B
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5. How do Neural Networks Learn.mp4
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112.2 MB
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5. How do Neural Networks Learn.srt
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5. LSTM Practical Intuition.mp4
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187.4 MB
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5. LSTM Practical Intuition.srt
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21 KB
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5. Setting up a distributed strategy.mp4
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5. Setting up a distributed strategy.srt
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5. Step 2 - Max Pooling.mp4
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140.2 MB
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5. Step 2 - Max Pooling.srt
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21 KB
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5. Temporal Difference.mp4
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5. Temporal Difference.srt
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5. Training, evaluating the model.mp4
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15.2 MB
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5. Training, evaluating the model.srt
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6. AI Trader - Step 5.mp4
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33.2 MB
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6. AI Trader - Step 5.srt
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6. Adding a custom head to the pre-trained model.mp4
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19.7 MB
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6. Adding a custom head to the pre-trained model.srt
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4.2 KB
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6. Artificial Neural Network Quiz.html
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102.4 B
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6. Deep Q-Learning Intuition - Step 1.mp4
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99.9 MB
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6. Deep Q-Learning Intuition - Step 1.srt
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22.4 KB
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6. Defining a distributed model.mp4
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12.5 MB
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6. Defining a distributed model.srt
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6. Gradient Descent.mp4
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60.6 MB
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6. Gradient Descent.srt
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14 KB
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6. HOMEWORK SOLUTION Convolutional Neural Networks.html
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614.4 B
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6. LSTM Variations.mp4
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6. LSTM Variations.srt
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6. Preparing Schema for production.mp4
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19.7 MB
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6. Preparing Schema for production.srt
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6. Saving the model for production.mp4
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25.4 MB
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6. Saving the model for production.srt
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6. Saving the model.mp4
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6. Saving the model.srt
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6. Starting the Flask application.mp4
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27.6 MB
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6. Starting the Flask application.srt
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6. Step 3 - Flattening.mp4
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6. Step 3 - Flattening.srt
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6. What's next.html
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2.1 KB
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7. Dataset Loader function.mp4
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38.9 MB
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7. Dataset Loader function.srt
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8 KB
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7. Deep Q-Learning Intuition - Step 2.mp4
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7. Deep Q-Learning Intuition - Step 2.srt
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7. Defining the transfer learning model.mp4
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7. Defining the transfer learning model.srt
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7. Final evaluation - Speed test normal model vs distributed model.mp4
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28.4 MB
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7. Final evaluation - Speed test normal model vs distributed model.srt
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4.4 KB
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7. HOMEWORK Artificial Neural Networks.html
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512 B
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7. Saving the Schema.mp4
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7. Saving the Schema.srt
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1.8 KB
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7. Sending API requests over internet to the model.mp4
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35 MB
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7. Sending API requests over internet to the model.srt
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4.5 KB
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7. Serving the TensorFlow 2.0 Model.mp4
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27.9 MB
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7. Serving the TensorFlow 2.0 Model.srt
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4.9 KB
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7. Step 4 - Full Connection.mp4
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194.1 MB
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7. Step 4 - Full Connection.srt
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7. Stochastic Gradient Descent.mp4
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67.2 MB
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7. Stochastic Gradient Descent.srt
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12.3 KB
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7. TensorFlow Lite Converter.mp4
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6.3 MB
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7. TensorFlow Lite Converter.srt
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8. Backpropagation.mp4
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43.1 MB
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8. Backpropagation.srt
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8. Compiling the Transfer Learning model.mp4
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12.6 MB
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8. Compiling the Transfer Learning model.srt
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3.5 KB
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8. Converting the model to a TensorFlow Lite model.mp4
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4.9 MB
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8. Converting the model to a TensorFlow Lite model.srt
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1.4 KB
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8. Creating a JSON object.mp4
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23.6 MB
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8. Creating a JSON object.srt
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8. Experience Replay.mp4
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114.7 MB
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8. Experience Replay.srt
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23.9 KB
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8. HOMEWORK SOLUTION Artificial Neural Networks.html
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409.6 B
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8. State creator function.mp4
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32.3 MB
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8. State creator function.srt
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8. Summary.mp4
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30.3 MB
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8. Summary.srt
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6 KB
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8. What's next.html
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2 KB
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8.1 Yahoo finance - APPLE stocks.html
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102.4 B
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9. Action Selection Policies.mp4
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136.9 MB
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9. Action Selection Policies.srt
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24 KB
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9. Image Data Generators.mp4
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32.6 MB
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9. Image Data Generators.srt
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6.3 KB
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9. Loading the dataset.mp4
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10 MB
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9. Loading the dataset.srt
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1.8 KB
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9. Saving the converted model.mp4
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8.7 MB
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9. Saving the converted model.srt
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1.9 KB
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9. Sending the first POST request to the model.mp4
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27.3 MB
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9. Sending the first POST request to the model.srt
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9. Softmax & Cross-Entropy.mp4
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117.8 MB
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9. Softmax & Cross-Entropy.srt
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[CourseClub.Me].url
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[DesireCourse.Net].url
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![Udemy - A Complete Guide on TensorFlow 2.0 using Keras API [Desire Course]](/static/img/no-torrent-image.png "Udemy - A Complete Guide on TensorFlow 2.0 using Keras API [Desire Course]")
