Results for "using keras as api for tensorflow"
Imperial College London
Skills you'll gain: Tensorflow, Generative AI, Recurrent Neural Networks (RNNs), Autoencoders, Generative Model Architectures, Data Pipelines, Keras (Neural Network Library), Model Evaluation, Deep Learning, Image Analysis, Model Training, Bayesian Network, Transfer Learning, Convolutional Neural Networks, Computer Programming, Model Optimization, Data Validation, Applied Machine Learning, Bayesian Statistics, Supervised Learning
Skills you'll gain: Transfer Learning, Model Evaluation, PyTorch (Machine Learning Library), Fine-tuning, Vision Transformer (ViT), Keras (Neural Network Library), Deep Learning, Logistic Regression, Convolutional Neural Networks, Reinforcement Learning, Model Optimization, Autoencoders, Generative AI, Model Training, Unsupervised Learning, Tensorflow, Applied Machine Learning, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs)
Skills you'll gain: Tensorflow, Model Optimization, Keras (Neural Network Library), Model Training, MLOps (Machine Learning Operations), Data Pipelines, Performance Tuning, Data Preprocessing, Data Import/Export
DeepLearning.AI
Skills you'll gain: Tensorflow, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Computer Vision, Model Evaluation, Transfer Learning, Image Analysis, Keras (Neural Network Library), Natural Language Processing, Model Training, Time Series Analysis and Forecasting, Deep Learning, Embeddings, Fine-tuning, Artificial Neural Networks, Applied Machine Learning, Generative AI, Classification Algorithms, Forecasting, Machine Learning
Skills you'll gain: OAuth, Unit Testing, Apache Airflow, Fine-tuning, Model Evaluation, Data Validation, API Design, Test Driven Development (TDD), Data Integrity, Extract, Transform, Load, Multimodal Prompts, Natural Language Processing, MLOps (Machine Learning Operations), OpenAI API, Data Presentation, Hugging Face, Token Optimization, LLM Application, CI/CD, Tensorflow
Skills you'll gain: Keras (Neural Network Library), Convolutional Neural Networks, Reinforcement Learning, Deep Learning, Model Optimization, Autoencoders, Generative AI, Unsupervised Learning, Tensorflow, Generative Adversarial Networks (GANs), Generative Model Architectures, Transfer Learning, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Model Training, Time Series Analysis and Forecasting
What brings you to Coursera today?
DeepLearning.AI
Skills you'll gain: Generative Adversarial Networks (GANs), Autoencoders, Generative AI, Tensorflow, Computer Vision, Image Analysis, Transfer Learning, Convolutional Neural Networks, Generative Model Architectures, Fine-tuning, Deep Learning, Keras (Neural Network Library), Model Training, Model Optimization, Applied Machine Learning, Artificial Neural Networks, Classification Algorithms, Model Evaluation, Distributed Computing, Visualization (Computer Graphics)
Skills you'll gain: Embeddings, Natural Language Processing, Keras (Neural Network Library), Generative AI, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks (RNNs), Model Evaluation, Generative Model Architectures, Image Analysis, Model Training, Artificial Neural Networks, Text Mining, Computer Vision, Model Optimization, Data Preprocessing, Fine-tuning, Tensorflow, Deep Learning, Model Deployment
Skills you'll gain: Keras (Neural Network Library), Deep Learning, Transfer Learning, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Model Optimization, Machine Learning Methods, Image Analysis, Applied Machine Learning, Autoencoders, Model Training, Regression Analysis, Network Architecture, Natural Language Processing, Machine Learning
Skills you'll gain: Model Evaluation, Image Analysis, Computer Vision, Vision Transformer (ViT), Tensorflow, Keras (Neural Network Library), AI Workflows, Model Training, Embeddings, Deep Learning, Large Language Modeling, Natural Language Processing, Model Deployment, Model Optimization, Data Pipelines, Risk Modeling, Data Preprocessing
Skills you'll gain: Feature Engineering, Model Deployment, Data Preprocessing, Machine Learning Methods, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Machine Learning, Test Script Development, Model Evaluation, Unsupervised Learning, Containerization, Data Processing, Data Wrangling, Unit Testing, Development Testing, Software Development, Python Programming, Application Programming Interface (API)
Skills you'll gain: Model Context Protocol, Load Balancing, Containerization, Docker (Software), Model Deployment, Restful API, API Design, Application Programming Interface (API), Authentications, Application Deployment, Tool Calling, Scalability, Performance Tuning, Data Validation
In summary, here are 10 of our most popular using keras as api for tensorflow courses
- TensorFlow 2 for Deep Learning: Imperial College London
- IBM Deep Learning with PyTorch, Keras and Tensorflow: IBM
- Build & Optimize TensorFlow ML Workflows: Coursera
- DeepLearning.AI TensorFlow Developer: DeepLearning.AI
- Tokens to Deployment: NLP, Language Models, & Production API: Coursera
- Deep Learning with Keras and Tensorflow: IBM
- TensorFlow: Advanced Techniques: DeepLearning.AI
- Keras Deep Learning Projects with TensorFlow: EDUCBA
- Introduction to Deep Learning & Neural Networks with Keras: IBM
- Building Vision and NLP Workflows with TensorFlow pipelines: Coursera
