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Python with DL

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                Deep Learning

Overview:

Deep learning could also be a machine learning technique that teaches computers to do and do what comes naturally to humans: learn by example. Python with DL Deep learning could also be a key technology behind driverless cars, facultative them to acknowledge a stop sign, or to inform apart a pedestrian from a post.  Deep learning is getting innumerous attention late and for good reason. It’s achieving results that weren’t potential before.

 

Deep learning refers to every deep neural networks and totally different branches of machine learning like deep reinforcement learning. inside the press, it forever suggests that deep neural nets.

Neural networks unit a bunch of algorithms, well-endowed loosely once the human brain, that unit designed to acknowledge patterns. They interpret sensory data through a kind of machine perception, labeling or bunch raw input. The patterns they acknowledge unit numerical, contained in vectors, into that each one real-world data, be it footage, sound, text or datum, ought to be translated.

Neural networks facilitate USA cluster and classify. you may think about them as a bunch and classification layer on high of knowledge you store and manage. they assist to cluster untagged data in step with similarities among the instance inputs, which they classify data when they need a tagged dataset to educate on. (To be extra precise, neural networks extract choices that unit fed to totally different algorithms for bunch and classification; so you may think about deep neural networks as elements of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

The “deep” refers to neural nets with quite one hidden layer.

The depth of the neural net permits it to construct a feature hierarchy of skyrocketing abstraction, with each consequent layer acting as a filter for added and extra advanced choices that blend those of the previous layer. This feature hierarchy and additionally the filters that model significance inside the data, is made automatically once deep nets learn to reconstruct unattended data. as a results of they will work with unattended data, that constitutes the majority of knowledge inside the planet, deep nets can become extra correct than ancient metric capacity unit algorithms that unit unable to handle unattended data. That is, the algorithms with access to extra data win.

Deep learning maps inputs to outputs. It finds correlations. it’s referred to as a “universal approximator”, as a results of it’ll learn to approximate the perform f(x) = y between any input x and any output y, forward they are connected through correlation or exploit in any respect.

 

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What Will You Learn?

  • Feature Generation Automation.
  • Works Well With Unstructured Data.
  • Better Self-Learning Capabilities.
  • Supports Parallel and Distributed Algorithms.
  • Cost Effectiveness.
  • Advanced Analytics.
  • Scalability.

Course Content

Introduction to Tensorflow and Colab

  • Tensorflow Variables
  • Basic tensorflow operations
  • Eager Execution
  • Using GPU on Colab

Introduction to Neural Network

Activation Function

Optimizers

Loss Functions

Backpropagation

ANN (Artificial Neural Networks)

CNN (Convolution Neural Network)

Transfer Learning

RNN (Recurrent Neural Network)

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