Summaries for Advanced Learning Algorithms (2)

 TensorFlow implementation



How data is represented in Numpy and Tensorflow ?








FYI:

TensorFlow: An Overview

TensorFlow is an open-source machine learning library developed by Google that is widely used for deep learning, neural networks, and large-scale machine learning tasks. It provides tools for building, training, and deploying machine learning models efficiently.

1. Key Features of TensorFlow

  • Scalability: Supports CPUs, GPUs, and TPUs (Tensor Processing Units).
  • Flexibility: Works for both simple models (linear regression) and complex deep learning architectures (CNNs, RNNs, Transformers).
  • Eager Execution & Graph Computation: Allows dynamic computation (eager mode) and optimized execution (graph mode).
  • TensorFlow Extended (TFX): Tools for deploying ML models in production.
  • Integration with Keras: TensorFlow supports Keras as a high-level API for building deep learning models.


Building a neural network in TensorFlow 

simplify the code above 





 Neural network implementation in Python




Is there a path to AGI ?


How neural networks are implemented efficiently ?


Vectorization 









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