A list of tools you should be using for your deep learning projects
TensorBoard: TensorFlow’s visualization toolkit
TensorBoard provides the visualization and tooling needed for machine learning experimentation:
- Tracking and visualizing metrics such as loss and accuracy
- Visualizing the model graph (ops and layers)
- Viewing histograms of weights, biases, or other tensors as they change over time
- Projecting embeddings to a lower dimensional space
- Displaying images, text, and audio data
- Profiling TensorFlow programs
The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for all developers, regardless of their deep learning framework of choice.
The Gluon API offers a flexible interface that simplifies the process of prototyping, building, and training deep learning models without sacrificing training speed.
IBM Watson Studio
IBM Watson Studio empowers organizations to tap into data assets and inject predictions into business processes and modern applications.
It’s suited for hybrid multicloud environments that demand mission-critical performance, security and governance — in public clouds, in private clouds, on-premises and on the desktop, including IBM Cloud Pak for Data.
Ludwig is a toolbox that allows to train and test deep learning models without the need to write code.
A new data type-based approach to deep learning model design that makes the tool suited for many different applications.
Experienced users have deep control over model building and training, while newcomers will find it easy to use.
Spell is an intuitive, deep learning platform that manages your infrastructure and projects, making machine learning projects easier to start and faster to get results.
Use Jupyter Notebooks or Jupyter Labs powered by Spell’s GPUs, and easily collaborate on a notebook within an organization.
Spell allows teams to experiment faster, increases collaboration and provides full project transparency.