Tensorflow Training


TENSORFLOW TRAINING COURSE MODULES


Section 1: Introduction to Deep Learning with TensorFlow

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Discuss the idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go Deep
  • Real-Life use cases of Deep Learning
  • Scenarios where Deep Learning is applicaScalarsble
  • The Math behind Machine Learning: Linear Algebra
    • Scalars
    • Vectors
    • Matrices
    • Tensors
    • Hyperplanes
  • The Math Behind Machine Learning: Statistics
    • Probability
    • Conditional Probabilities
    • Posterior Probability
    • Distributions
    • Samples vs Population
    • Resampling Methods
    • Selection Bias
    • Likelihood
  • Review of Machine Learning Algorithms
    • Regression
    • Classification
    • Clustering
    • Reinforcement Learning
    • Underfitting and Overfitting
    • Optimization
    • Convex Optimization

Section 2: Fundamentals of Neural Networks

  • Defining Neural Networks
  • The Biological Neuron
  • The Perceptron
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Backpropagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Activation Functions
  • Linear
  • Sigmoid
  • Tanh
  • Hard Tanh
  • Softmax
  • Rectified Linear
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters
  • Learning Rate
  • Regularization
  • Momentum
  • Sparsity

Section 3: Fundamentals of Deep Networks

  • Defining Deep Learning
  • Defining Deep Networks
  • Common Architectural Principals of Deep Networks
  • Reinforcement Learning application in Deep Networks
  • Parameters
  • Layers
  • Activation Functions – Sigmoid, Tanh, ReLU
  • Loss Functions
  • Optimization Algorithms
  • Hyperparameters

Section 4: Introduction to TensorFlow

  • What is TensorFlow?
  • Use of TensorFlow in Deep Learning
  • Working of TensorFlow
  • How to install Tensorflow
  • HelloWorld with TensorFlow
  • Running a Machine learning algorithms on TensorFlow

Section 5: Convolutional Neural Networks (CNN)

  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

Section 6: Recurrent Neural Networks (RNN)

  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

Section 7: Restricted Boltzmann Machine(RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Variational Autoencoders
  • Deep Belief Network

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