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TensorFlow Training

About TensorFlow:

TensorFlow is a powerful high performance numerical computation open source software library collection. No Matter what platforms (CPUs, GPUs, TPUs) you have, its flexible architecture allows easy deployment of computation across a variety of platform including desktops, clusters of servers or mobile devices. It includes machine learning and deep learning with flexible numerical computation core. With BISP you gain 100% real life examples of TensorFlow and learn how to solve real life problems. Our trainer and technical support team ensure your queries are address on timely manner.

Course description:

This course focuses on deep learning with best approach to building artificial intelligence algorithms. Starting from basics components of deep learning (what it means, how it works) to advance code develop development necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. You will learn how to apply these algorithms for exploring creative applications and solving complex business scenarios. The training is all about training computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image. Our practical approach applications along with guided homework assignments , you'll be expected to create datasets, develop and train neural networks, explore your own media collections, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.

Introduction to Deep Learning

  • What is Deep Learning
  • Limitations of Machine Learning
  • The core idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • Real-Life use cases of Deep Learning
  • Applications of Deep Learning

Getting Started with TensorFlow

  • What is TensorFlow?
  • TensorFlow code-basics
  • Hello World with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression
  • Activation Functions

Basics of Defining Neural Networks

  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step - Use-Case Implementation
  • 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
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters
  • Learning Rate
  • Regularization
  • Momentum
  • Sparsity

Convolutional Neural Networks (CNN)

  • Main concepts of CNNs
  • CNNs in action
  • LeNet5
  • Implementing a LeNet-5 step by step
  • Dataset preparation
  • Fine-tuning implementation
  • Inception-v3
  • Emotion recognition with CNNs

Optimizing TensorFlow Autoencoders

  • How does an autoencoder work?
  • Implementing autoencoders with TensorFlow
  • Improving autoencoder robustness
  • Fraud analytics with autoencoders

Recurrent Neural Networks

  • Working principles of RNNs
  • RNN and the gradient vanishing-exploding problem
  • Implementing an RNN for spam prediction
  • Developing a predictive model for time series data
  • An LSTM predictive model for sentiment analysis
  • Human activity recognition using LSTM model

Heterogeneous And Distributed Computing

  • GPGPU computing
  • The TensorFlow GPU setup
  • Distributed computing
  • The distributed TensorFlow setup

Advanced TensorFlow Programming

  • tf.estimator
  • TFLearn
  • PrettyTensor
  • Keras

Recommendation Systems Using Factorization Machines

  • Recommendation systems
  • Movie recommendation using collaborative filtering
  • Factorization machines for recommendation systems
  • Improved factorization machines

Reinforcement Learning

  • The RL problem
  • OpenAI Gym
  • The Q-Learning algorithm
  • Deep Q-learning
Course Id:
Course Fees:
301 USD