We talk about how you can train your models on the cloud with a p2.xlarge instance, leveraging GPU computation power. If you like the video, like and SUBSCRIBE to the channel for more amazing content!

LINKS

Create an account: https://aws.amazon.com
Instances & AMI: https://docs.aws.amazon.com/AW....SEC2/latest/UserGuid

Watch live to learn about how the deep learning frameworks in MATLAB and Simulink can be used with TensorFlow and PyTorch to provide enhanced capabilities for building and training your Machine Learning model.

Heather Gorr, PhD and Yann Debray will show you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

Explore the options and benefits, along with examples, of the various interoperability pathways available, including:
- Importing and exporting models from TensorFlow, PyTorch, and ONNX into and from MATLAB
- Coexecuting MATLAB alongside installations of TensorFlow and PyTorch

Reviewing Lambda and Razer's Tensorbook, a laptop aimed at deep learning, with 16GB of VRAM (GPU memory), 64GB of RAM, 2TB of NVMe storage and an 8-core intel i7 11800H CPU.
https://lambdalabs.com/deep-le....arning/laptops/tenso

Neural Networks from Scratch book: https://nnfs.io
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Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we're going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices.

Text tutorials and sample code: https://pythonprogramming.net/....cryptocurrency-recur

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