Cursos de Deep Learning

4 horas

25 capítulos

Avançado

6 ESTUDANDO AGORA

Habilidades adquiridas: Neural Networks, Model Training and Evaluation, Data Preprocessing, Hyperparameter Tuning, Machine Learning with scikit-learn

5 horas

29 capítulos

Avançado

2 ESTUDANDO AGORA

Habilidades adquiridas: Natural Language Processing, Natural Language Handling

2 horas

16 capítulos

Intermediário

5 ESTUDANDO AGORA

Habilidades adquiridas: TensorFlow Basics, Neural Networks, Python Data Structures, Data Preprocessing

3 horas

22 capítulos

Intermediário

2 ESTUDANDO AGORA

Habilidades adquiridas: Understanding RNNs, LSTMs, and GRUs, Implementing recurrent networks in PyTorch, Processing time series and sequential data, Applying RNNs to NLP tasks (sentiment analysis) , End-to-end model development and evaluation

5 horas

51 capítulos

Iniciante

12 ESTUDANDO AGORA

Habilidades adquiridas: Functions & Sets, Series Analysis , Limits & Derivatives , Integrals , Gradient Descent , Vectors & Matrices , Linear Transformations , Matrix Decomposition , Probability Rules , Bayes' Theorem, Statistical Measures , Probability Distributions

1 hora

12 capítulos

Iniciante

2 ESTUDANDO AGORA

Habilidades adquiridas: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building

1 hora

9 capítulos

Iniciante

6 ESTUDANDO AGORA

Habilidades adquiridas: Prompt Engineering Fundamentals , Role and Context Prompting , Few-Shot Prompting , Chain-of-Thought Prompting , Structured Output Design , Prompt Refinement , Prompt Evaluation

3 horas

20 capítulos

Avançado

4 ESTUDANDO AGORA

Habilidades adquiridas: PyTorch Basics, Neural Networks, Model Training and Evaluation

4 horas

29 capítulos

Intermediário

2 ESTUDANDO AGORA

Habilidades adquiridas: Image Processing with OpenCV, Convolutional Neural Networks, Object Detection Approaches

2 horas

15 capítulos

Intermediário

1 ESTUDANDO AGORA

Habilidades adquiridas: Mathematical Foundations of Loss Functions, Risk Minimization Theory, Regression Loss Analysis, Classification Loss Analysis, Information-Theoretic Losses, Loss Function Selection and Comparison

1 hora

12 capítulos

Iniciante

Habilidades adquiridas: AI Ethics Fundamentals , Ethical Decision-Making , Fairness and Bias Analysis , Transparency Principles , Accountability in AI , Data Privacy Concepts , Responsible AI Frameworks , Regulatory Awareness

2 horas

16 capítulos

Intermediário

3 ESTUDANDO AGORA

Habilidades adquiridas: Classification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) , Regression metrics (MSE, RMSE, MAE, R²) , Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) , Dimensionality reduction evaluation , Anomaly detection evaluation , Cross-validation techniques