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Deep Learning with TensorFlow

Traditional neural networks depend on shallow nets, consisting of one input, one hidden layer and one output layer. Deep-learning networks are very different from these ordinary neural networks having more hidden layers, or so-called more depth

Such kinds of nets are capable of discovering unseen structures within unlabelled and unstructured data (i.e. images, sound, and text), which establishes the vast majority of data in the world

TensorFlow is one among the finest libraries to implement deep learning. Nodes present in the graph signify mathematical operations, while the edges signify the multidimensional data arrays /tensors that flow between them

5 Days

  • Basic programming knowledge in Python 
  • A few Concepts about Machine Learning
  • HelloWorld with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression
  • Activation Functions
  • CNN History
  • Understanding CNNs
  • CNN Application
  • Intro to RNN Model
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
  • Applications of Unsupervised Learning
  • Restricted Boltzmann Machine
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders and Applications
  • Autoencoders
  • Deep Belief Network