TensorFlow
A library for numerical computation using data flow graphs, enabling the creation and deployment of machine learning models
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| + | High-Level APIs | Simplifies model training and iteration with high-level APIs like Keras. |
|---|---|---|
| + | Distributed Training | Supports distributed training across multiple machines, improving scalability. |
| + | Pre-trained Models | Access pre-trained models from TensorFlow Hub and the Model Garden for various tasks. |
| + | Deployment Flexibility | Deploy models on servers, edge devices, browsers, mobile, and even microcontrollers. |
| + | Visualization | TensorBoard helps in monitoring and analyzing model performance, making it easier to experiment with machine learning models. |
| + | Automatic Differentiation | Automatically computes gradients, a critical step in training neural networks |
| + | Eager Execution | Allows for immediate execution of code and visualization of results, enabling rapid prototyping and debugging |
| + | Modularity | Offers modular design and flexibility, allowing users to build custom models and adapt them to specific tasks. |
| + | Efficient GPU/CPU Computing | Write code once and execute it seamlessly on both GPU and CPU architectures. |
| + | Data Validation and Transformation | Provides tools to preprocess, validate, and transform large datasets. |
| + | Deep Neural Networks | Excels in training deep neural networks for tasks like image recognition and word embedding. |
| + | Word Embedding | Create meaningful representations of words using its embedding layers. |
| + | Recurrent Neural Networks (RNNs) | Supports RNNs for sequence-based tasks. |
| - | Speed | It is slower compared to its competing frameworks. |
| - | GPU support | Only supports NVIDIA GPUs and Python for GPU programming. |
| - | Architectural limitation | Its TPU architecture allows only execution of models and doesn’t allow its training |
| - | Inconsistent | A single name is used for various different purposes which makes it difficult for a user to remember and use |
System Requirements
| # | Minimum | Recommended |
|---|---|---|
| 1 |
| |
| 2 |
| |
| 3 |
| TensorRT to improve latency and throughput for inference. |
| 4 | NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher |
Ratings
4.675
| Capterra | 4.55 based on 43 reviews |
|---|---|
| G2CROWD | 4.55 based on 23 reviews |
| InfoWorld | 5.05 based on professional's opinion |
Repository
License
Categories
Alternatives
Machine Learning
Artificial Intelligence
Deep Learning
Artificial Intelligence
Deep Learning
Notes
- TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.