Advanced Skill Certificate in Keras for Neural Networks
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- Building Custom Keras Layers and Models
- Advanced Optimizer Techniques in Keras: AdamW, SGD with Momentum
- Keras Tuner for Hyperparameter Optimization
- Implementing Neural Network Regularization Techniques in Keras: Dropout, Batch Normalization, Weight Decay
- Deep Learning with Keras: Convolutional Neural Networks (CNNs) for Image Classification
- Recurrent Neural Networks (RNNs) and LSTMs with Keras for Sequence Data
- Keras and Transfer Learning for Efficient Model Building
- Deploying Keras Models: Production-Ready Deployment Strategies
- Keras with TensorFlow: Leveraging TensorFlow's functionalities
- Advanced Keras Metrics and Loss Functions
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Role Description Senior Keras Engineer (Deep Learning, Neural Networks) Lead the development and implementation of complex neural network models using Keras, contributing to cutting-edge AI solutions within a large-scale organization.
Keras Developer (TensorFlow, Machine Learning) Design, build, and deploy efficient and scalable machine learning models using Keras, working collaboratively with data scientists and engineers on impactful projects.
AI/ML Engineer (Python, Keras, Deep Learning) Develop and maintain AI and machine learning systems utilizing Keras, contributing to innovative applications across various industries such as finance and healthcare.
Data Scientist (Keras, Neural Networks, Data Analysis) Apply advanced statistical methods and Keras-based neural networks to analyze large datasets, extract meaningful insights, and build predictive models.
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- ThreeFourHoursPerWeek
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