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- 16. Anexo III - Redes Neurais Recorrentes - teoria/4. Intuição prática.mp4 166.4 MB
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- 16. Anexo III - Redes Neurais Recorrentes - teoria/1. O que são Redes Neurais Recorrentes.mp4 103.1 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/2. A Equação de Bellman.mp4 95.1 MB
- 14. Anexo I - Redes Neurais Artificiais - teoria/9. Funções de ativação II.mp4 81.5 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/2. Introdução a redes neurais convolucionais II.mp4 80.5 MB
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- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/11. Replay de Experiência.mp4 75.2 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/12. Políticas de Seleções de Ações.mp4 74.6 MB
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- 16. Anexo III - Redes Neurais Recorrentes - teoria/2. Problema do gradiente desaparecendo (vanish gradient problem).mp4 71.0 MB
- 10. API com Flask e TensorFlow 2.0/1. Configuração do Projeto.mp4 70.3 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/4. Markov Decision Process - MDP.mp4 70.3 MB
- 5. Redes Neurais Recorrentes/1. Configuração do Projeto e Pré-processamento.mp4 66.1 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/6. Etapa 3 - flattening.mp4 65.8 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/7. Etapa 4 - rede neural densa.mp4 65.3 MB
- 9. Pré-processamento de Dados com TFT (TensorFlow Transform)/5. Pipeline de Pré-processamento.mp4 63.7 MB
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- 16. Anexo III - Redes Neurais Recorrentes - teoria/4. Intuição prática.mp4 166.4 MB
- 16. Anexo III - Redes Neurais Recorrentes - teoria/3. Long short term memory - LSTM.mp4 104.1 MB
- 16. Anexo III - Redes Neurais Recorrentes - teoria/1. O que são Redes Neurais Recorrentes.mp4 103.1 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/2. A Equação de Bellman.mp4 95.1 MB
- 14. Anexo I - Redes Neurais Artificiais - teoria/9. Funções de ativação II.mp4 81.5 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/2. Introdução a redes neurais convolucionais II.mp4 80.5 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/1. Introdução a redes neurais convolucionais I.mp4 75.9 MB
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- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/11. Replay de Experiência.mp4 75.2 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/12. Políticas de Seleções de Ações.mp4 74.6 MB
- 2. Básico do TensorFlow/1. Do TensorFlow 1.x para o TensorFlow 2.0 - Constantes, variáveis e tensores.mp4 72.9 MB
- 1. Introdução/1. Bem-vindo(a) ao curso!.mp4 72.3 MB
- 16. Anexo III - Redes Neurais Recorrentes - teoria/2. Problema do gradiente desaparecendo (vanish gradient problem).mp4 71.0 MB
- 10. API com Flask e TensorFlow 2.0/1. Configuração do Projeto.mp4 70.3 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/4. Markov Decision Process - MDP.mp4 70.3 MB
- 5. Redes Neurais Recorrentes/1. Configuração do Projeto e Pré-processamento.mp4 66.1 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/6. Etapa 3 - flattening.mp4 65.8 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/7. Etapa 4 - rede neural densa.mp4 65.3 MB
- 9. Pré-processamento de Dados com TFT (TensorFlow Transform)/5. Pipeline de Pré-processamento.mp4 63.7 MB
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- 4. Section 4/2. Convolution in CNN (part1).mp4 143.9 MB
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- 2. Section 2/6. Logistic Regression - Implementation.mp4 123.0 MB
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- 1. Section 1/6. Intro to Machine Learning.mp4 117.9 MB
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- 16. Anexo III - Redes Neurais Recorrentes - teoria/4. Intuição prática.mp4 166.4 MB
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- 16. Anexo III - Redes Neurais Recorrentes - teoria/1. O que são Redes Neurais Recorrentes.mp4 103.1 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/2. A Equação de Bellman.mp4 95.1 MB
- 14. Anexo I - Redes Neurais Artificiais - teoria/9. Funções de ativação II.mp4 81.5 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/2. Introdução a redes neurais convolucionais II.mp4 80.5 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/1. Introdução a redes neurais convolucionais I.mp4 75.9 MB
- 7. Aprendizagem por Reforço Profunda para Negociação no Mercado de Ações/12. Treinamento II.mp4 75.6 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/11. Replay de Experiência.mp4 75.2 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/12. Políticas de Seleções de Ações.mp4 74.6 MB
- 2. Básico do TensorFlow/1. Do TensorFlow 1.x para o TensorFlow 2.0 - Constantes, variáveis e tensores.mp4 72.9 MB
- 1. Introdução/1. Bem-vindo(a) ao curso!.mp4 72.3 MB
- 16. Anexo III - Redes Neurais Recorrentes - teoria/2. Problema do gradiente desaparecendo (vanish gradient problem).mp4 71.0 MB
- 10. API com Flask e TensorFlow 2.0/1. Configuração do Projeto.mp4 70.3 MB
- 17. Anexo IV - Aprendizagem por Reforço Profunda - teoria/4. Markov Decision Process - MDP.mp4 70.3 MB
- 5. Redes Neurais Recorrentes/1. Configuração do Projeto e Pré-processamento.mp4 66.1 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/6. Etapa 3 - flattening.mp4 65.8 MB
- 15. Anexo II - Redes Neurais Convolucionais - teoria/7. Etapa 4 - rede neural densa.mp4 65.3 MB
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- 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4 88.9 MB
- 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4 61.0 MB
- 02.Introduction to neural networks/0212.N-parameter gradient descent.mp4 60.4 MB
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- 03.Setting up the working environment/0306.Installing TensorFlow 2.mp4 53.7 MB
- 02.Introduction to neural networks/0201.Introduction to neural networks.mp4 48.0 MB
- 12.Business case/1204.Preprocessing the data.mp4 46.7 MB
- 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4 44.3 MB
- 05.TensorFlow - An introduction/0501.TensorFlow outline.mp4 44.0 MB
- 02.Introduction to neural networks/0203.Types of machine learning.mp4 42.8 MB
- 10.Preprocessing/1003.Standardization.mp4 42.3 MB
- 13.Conclusion/1305.An overview of non-NN approaches.mp4 42.1 MB
- 01.Welcome! Course introduction/0102.What does the course cover.mp4 41.0 MB
- 13.Conclusion/1301.See how much you have learned.mp4 40.8 MB
- 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4 39.8 MB
- 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4 39.8 MB
- 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4 39.7 MB
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- 3.3. Training TensorFlow Applications/18.Linear regression in code - Part 2.mp4 23.2 MB
- 5.5. Machine Learning with Estimators/33.Coding a DNN estimator - Part 2.mp4 17.7 MB
- 5.5. Machine Learning with Estimators/35.Estimator automation in practice.mp4 16.6 MB
- 5.5. Machine Learning with Estimators/32.Coding a DNN estimator - Part 1.mp4 16.1 MB
- 4.4. Accessing Data with Datasets/26.Reading MNIST data in code.mp4 15.1 MB
- 3.3. Training TensorFlow Applications/20.Using TensorBoard in practice.mp4 13.7 MB
- 4.4. Accessing Data with Datasets/22.Coding with datasets and iterators.mp4 12.9 MB
- 6.6. Deploying Estimators to the Machine Learning Engine/41.Deploying jobs to ML Engine.mp4 12.7 MB
- 5.5. Machine Learning with Estimators/30.Coding a simple estimator - Part 2.mp4 11.0 MB
- 3.3. Training TensorFlow Applications/15.Simple optimizer example.mp4 10.5 MB
- 3.3. Training TensorFlow Applications/14.Using optimizers.mp4 10.2 MB
- 2.2. Fundamentals of TensorFlow Development/11.Accessing graphs and sessions in code.mp4 9.9 MB
- 5.5. Machine Learning with Estimators/28.Describing data with feature columns.mp4 9.8 MB
- 6.6. Deploying Estimators to the Machine Learning Engine/36.Creating a GCP project.mp4 9.5 MB
- 4.4. Accessing Data with Datasets/21.Datasets and iterators.mp4 9.1 MB
- 5.5. Machine Learning with Estimators/27.Understanding estimators.mp4 8.9 MB
- 6.6. Deploying Estimators to the Machine Learning Engine/37.Installing the Cloud SDK.mp4 8.8 MB
- 4.4. Accessing Data with Datasets/24.Creating datasets from files.mp4 8.7 MB
- 4.4. Accessing Data with Datasets/23.Dataset operations.mp4 8.0 MB
- 3.3. Training TensorFlow Applications/12.Variables and logging.mp4 7.5 MB
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- 3.2. TensorFlow Basics/10.Operations, or ops.mp4 15.7 MB
- 3.2. TensorFlow Basics/11.Model introduction.mp4 14.2 MB
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- 3.2. TensorFlow Basics/12.Layers introduction.mp4 12.6 MB
- 4.3. Exploration of a Full Project/14.Exploration of the dataset.mp4 12.1 MB
- 3.2. TensorFlow Basics/09.Tensors and variables.mp4 12.0 MB
- 4.3. Exploration of a Full Project/16.Exploration of training the model.mp4 11.8 MB
- 4.3. Exploration of a Full Project/17.See the live example.mp4 11.6 MB
- 5.4. Advanced Subjects/18.Use Python-based models in JS.mp4 10.8 MB
- 2.1. Introduction and Setup/06.A TensorFlow demo.mp4 9.7 MB
- 5.4. Advanced Subjects/19.Convert SavedModel to web.mp4 7.8 MB
- 4.3. Exploration of a Full Project/15.Exploration of the models and layers.mp4 7.8 MB
- 3.2. TensorFlow Basics/08.Your first tensor.mp4 7.0 MB
- 1.Introduction/01.Learning TensorFlow.mp4 5.6 MB
- 4.3. Exploration of a Full Project/13.Import example project.mp4 5.3 MB
- 2.1. Introduction and Setup/05.Introduction to machine learning.mp4 4.8 MB
- 2.1. Introduction and Setup/03.Introduction to TensorFlow.mp4 3.3 MB
- 6.Conclusion/20.Next steps.mp4 1.9 MB
- 2.1. Introduction and Setup/04.Differences between versions.mp4 1.8 MB
- 1.Introduction/02.Course prerequisites.mp4 1.3 MB
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- 1.Convolutional Neural Networks for Computer Vision/06.Transfer Learning.mp4 352.0 MB
- 2.Applications of Recurrent Neural Networks/10.Processing Text Sequences with LSTM Networks.mp4 339.3 MB
- 2.Applications of Recurrent Neural Networks/08.Time Series Forecasting with RNN.mp4 327.8 MB
- 1.Convolutional Neural Networks for Computer Vision/04.Building an Image Classifier with CNNs.mp4 296.0 MB
- 2.Applications of Recurrent Neural Networks/09.Producing Word Embeddings for NLP Tasks.mp4 290.0 MB
- 1.Convolutional Neural Networks for Computer Vision/05.Building Better CNNs with Regularization.mp4 223.4 MB
- 1.Convolutional Neural Networks for Computer Vision/03.Defining Layers for Image Recognition.mp4 206.8 MB
- 4.Introduction to Reinforcement Learning/19.Using an Intelligent Agent.mp4 146.1 MB
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- 1.Convolutional Neural Networks for Computer Vision/02.Installation and Setup.mp4 99.4 MB
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- 4.Introduction to Reinforcement Learning/18.Training a Neural Network Policy.mp4 40.8 MB
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- 3.Application of Deep Learning to AI Problems/12.Introduction to Generative Adversarial Networks.mp4 16.1 MB
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- 18 - Demo - TensorFlow Hub and Retraining.mp4 31.3 MB
- 19 - Demo - Code Updates to Run the New Model.mp4 31.0 MB
- 17 - Demo - Preparing the Images.mp4 28.9 MB
- 27 - Demo - Using Facenet.mp4 23.6 MB
- 28 - Demo - Building the Classifier.mp4 20.6 MB
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- 16 - Demo - Transfer Learning.mp4 11.1 MB
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- 14 - Introduction.mp4 7.1 MB
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