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~Get Your Files Here !/04 - 3. Automatic Vectorization and Automatic Parallelization/03 - Comparing naive and manual batching with automatic vectorization.mp4 13.3 MB
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~Get Your Files Here !/01 - Introduction/03 - Why use JAX.mp4 10.2 MB
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~Get Your Files Here !/01 - Introduction/05 - JAX vs. TensorFlow vs. PyTorch.mp4 7.8 MB
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6. Módulo 1 - Regressão Logística/2. Regressão logística (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. RegLogisticaTeoria.mp4 1.2 GB
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18. Módulo 2 - AdaBoost/1. AdaBoost (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. ADAboostTeoria.mp4 1.1 GB
16. Módulo 2 - Random Forest/2. RandomForest (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. RandomForestTeoria.mp4 557.4 MB
8. Módulo 1 - KNN/1. KNN (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. KNN teoria.mp4 471.1 MB
20. Módulo 2 - Bagging/1. Bagging (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. BaggingTeoria.mp4 467.2 MB
1. Módulo 1 - Conceitos Básicos/3. Viés e Variância/1. Viés e Variância.mp4 464.8 MB
17. Módulo 2 - ExtraTrees/1. ExtraTrees (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. ETteoria.mp4 390.6 MB
1. Módulo 1 - Conceitos Básicos/4. Visão geral dos algoritmos de machine learning/1. Visão geral algoritmos ML.mp4 388.5 MB
2. Módulo 1 - Regressão Linear/1. Regressão linear (conceito + matemática o que o algoritmo faz debaixo dos panos)/1. A matemática do Gradiente Descendente & Regressão Linear (machine learning).mp4 382.1 MB
12. Módulo 1 - Testando seus conhecimentos/3. Solução do exercício prático final - Parte 2/1. 10_2_DesafioSol_2.mp4 327.2 MB
14. Módulo 2 - Aprendizado não supervisionado/3. Clustering K Means (encontrando o número ideal de clusters)/1. KmeansNumClusters.mp4 321.9 MB
10. Módulo 1 - Decision Trees/3. Decision Trees evitando overfitting/1. DToverfitting.mp4 306.2 MB
13. Módulo 2 - Dados missing com Regressão/1. Substituindo dados missing com um modelo de regressão (Teoria)/1. MissingRegressaoTeoria.mp4 274.1 MB
4. Módulo 1 - Outros modelos de regressão linear/3. Regularização L1, L2 e regressão Lasso/1. Regularização L1 L2.mp4 265.7 MB
3. Módulo 1 - Pré-processamento/3. Feature selection correlação (conceito)/1. NatiCorrelacao.mp4 237.7 MB
1. Módulo 1 - Conceitos Básicos/1. Como estudar esse curso/1. AberturaCurso.mp4 226.2 MB
3. Módulo 1 - Pré-processamento/1. Pré-processamento dados missing/1. Dados missing Nati_Pronto.mp4 204.3 MB
22. Módulo 2 - Testando seus conhecimentos/2. Solução do exercício prático final/1. ML_2_10_DESAFIOsol.mp4 183.1 MB
6. Módulo 1 - Regressão Logística/3. Regressão logística (aplicação prática em R resolvendo um problema)/1. 5_1_RegLog.mp4 177.3 MB

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2. Project 2 - Deep Learning Project Dog Breed Prediction (Kaggle dataset)/1. Dog Breed Prediction.mp4 397.0 MB
1. Project 1 - AI Project Web application for Object Identification/4. Integrating web application with deep learning backend.mp4 150.4 MB
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1. Project 1 - AI Project Web application for Object Identification/2. Backend creation using pretrained Keras model - Resnet50.mp4 124.6 MB
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1. Project 1 - AI Project Web application for Object Identification/3. Adding Form to django App & uploading image.mp4 96.9 MB
3. AI Project End to End Credit Card Fraud Detection/29. Module 5 Model behind REST using Flask.mp4 61.5 MB
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3. AI Project End to End Credit Card Fraud Detection/13. Module 2 Cassandra Installation.mp4 26.5 MB
3. AI Project End to End Credit Card Fraud Detection/22. Module 4 Class code for Model Config & Training.mp4 24.2 MB
3. AI Project End to End Credit Card Fraud Detection/18. Module 3 Connecting transformers & estimators using Pipeline.mp4 24.0 MB
3. AI Project End to End Credit Card Fraud Detection/26. Module 5 Model Persistence & Selection.mp4 23.3 MB
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2. Aspectos Diversos/4. Deep Learning.mp4 91.9 MB
4. Prática com Weka/2. Prática com Weka (Continuação).mp4 90.7 MB
2. Aspectos Diversos/1. Processo de Aprendizado de Uma Rede Neural.mp4 68.4 MB
2. Aspectos Diversos/3. Conceitos Adicionais Importantes.mp4 64.4 MB
3. Prática/4. Prática Exemplo 2.mp4 54.9 MB
4. Prática com Weka/1. Prática com Weka.mp4 45.4 MB
3. Prática/5. Prática Exemplo 3.mp4 42.7 MB
3. Prática/3. Prática Exemplo 1.mp4 38.4 MB
1. Introdução/2. Introdução Como o computador aprende!.mp4 36.4 MB
1. Introdução/1. Apresentação.mp4 27.2 MB
2. Aspectos Diversos/2. Inspiração Redes Neurais Naturais.mp4 20.1 MB
3. Prática/1. Prática Introdução.mp4 13.8 MB
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1. Introdução/3. Avaliação de Cursos.mp4 1.8 MB
1. Introdução/5.Deep Learning.pdf 1.0 MB
1. Introdução/4.Conceitos Adicionais.pdf 975.6 kB
1. Introdução/6.Práticas.pdf 511.4 kB
1. Introdução/3.Inspração.pdf 340.1 kB
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02-data/009-data-exploration-with-pandas-code-along.mp4 32.4 MB
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02-data/011-plotting-with-matplotlib.mp4 34.4 MB
02-data/012-unstructured-data.mp4 11.9 MB
02-data/013-images-and-sound-in-jupyter.mp4 14.7 MB
02-data/014-feature-engineering.mp4 6.1 MB
02-data/015-exercise-1-presentation.mp4 3.4 MB
02-data/016-exercise-1-solution.mp4 9.9 MB
02-data/017-exercise-2-presentation.mp4 2.0 MB
02-data/018-exercise-2-solution.mp4 14.2 MB
02-data/019-exercise-3-presentation.mp4 1.8 MB
02-data/020-exercise-3-solution.mp4 7.5 MB