Tensorflow Training

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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

    Tensorflow Training in Chennai – Reviews

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