TensorFlow - bobbae/gcp GitHub Wiki

Tensorflow is an awesome open source, powerful, portable machine learning library developed by Google that can work with very large datasets.

Keras is the high-level API of Tensorflow.

Kubeflow is a MLOps tool that can manage pipelines including Tensorflow.

https://www.kdnuggets.com/2018/05/wtf-tensor.html

Tensorflow.js is a library for machine learning in JavaScript.

Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.

https://www.tensorflow.org/js

Getting started with TensorFlow.js.

https://www.youtube.com/watch?v=WYvgP9LfvTg

The Tensorflow Hub lets you search and discover hundreds of trained, ready-to-deploy machine learning models in one place. Reuse trained models like BERT and Faster R-CNN with just a few lines of code.

https://tfhub.dev/

https://www.youtube.com/watch?v=SJ1LGUyw-Xg

Visualizing your model with tensorboard.

https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview

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

https://www.youtube.com/watch?v=qEQ-_EId-D0

https://blog.ml6.eu/a-vertex-ai-tensorboard-alternative-for-smaller-budgets-part-1-ab840d2a592a

Graphs are data structures that contain a set of tf.Operation objects, which represent units of computation; and tf.Tensor objects, which represent the units of data that flow between operations. They are defined in a tf.Graph context. Since these graphs are data structures, they can be saved, run, and restored all without the original Python code.

https://www.youtube.com/watch?v=qTYQEXsBb_E

Tensorflow Enterprise makes it easier to develop and deploy Tensorflow models on Google Cloud, by providing users with a set of products and services, which provide enterprise-grade support and cloud scale performance. Users can get the benefits of Tensorflow Enterprise by using the Tensorflow Enterprise Distribution on AI Platform Notebooks, AI Platform Deep Learning Containers and AI Platform Deep Learning VM Image.

https://cloud.google.com/Tensorflow-enterprise/docs

https://github.com/tensorflow/examples

https://www.tensorflow.org/tutorials

https://cloud.google.com/architecture/running-distributed-tensorflow-on-compute-engine

https://cloud.google.com/ai-platform/docs/getting-started-keras

https://blog.tensorflow.org/2020/08/train-your-tensorflow-model-on-google.html

https://cloud.google.com/blog/products/ai-machine-learning/optimizing-waze-ad-delivery-with-google-clouds-vertex-ai

https://blog.tensorflow.org/2020/12/getting-started-with-distributed-tensorflow-on-gcp.html

https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models

https://cloud.google.com/compute/docs/tutorials/ml-inference-t4

https://github.com/jtoy/awesome-Tensorflow

https://www.freecodecamp.org/news/how-to-use-tensorflow-for-computer-vision/

GSP418 Predict Housing Prices with Tensorflow and AI Platform