Deep Learning with TensorFlow Training

Live Online & Classroom Enterprise Training

Master core concepts of Deep Learning with Google's TensorFlow- a distributed scalable deep learning platform. Our Deep Learning course aids in building deep learning models suitable for different business domains in TensorFlow. Train your team on TensorFlow and Neural Net to solve complex organizational problems.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is Deep Learning with TensorFlow Course about?

SpringPeople’s Deep learning training gives you an in-depth understanding of the architecture of TensorFlow Core, API layers, and the use cases. Master unsupervised learning models, deep learning models and more. Right from installing and configuring TensorFlow, importing data, simple models to develop complex layered models and architectures to crunch huge data sets leveraging the distributed, robust and scalable machine learning framework from Google.

               

Learn to implement Keras on top of TensorFlow to experiment with deep neural networks and tune machine learning models to produce more successful results with our deep learning with TensorFlow course.


Work on different types of Deep Architectures: Convolutional Networks, Recurrent Networks, and Autoencoders, and further get familiar with the advanced concepts of Natural Language Processing. Also, gain practical exposure on text to speech processing.


Our cloud labs comprise guided exercises practice building handwritten digit recognition, deep learning, convolution and time-series models of Neural Networks. Gain hands-on experience by working with real-time uses cases and data sets using various neural network architecture, suitable to different industry domains and provide solutions.


Lead TensorFlow based AI projects with your teams trained in our TensorFlow course.

What are the objectives of Deep Learning with TensorFlow Course ?

The Deep Learning training enables you to:

 

  • Articulate the core architecture and API layers TensorFlow

  • Construct a computing environment and learn to install TensorFlow

  • Develop TensorFlow graphs required for everyday computations

  • Use logistic regression for classification along with TensorFlow

  • Develop, design and train  a multilayer neural network with TensorFlow

  • Demonstrate Activation functions and Optimizers in detail with hands-on

  • Demonstrate intuitively convolutional neural networks for image recognition

  • Design and construct a neural network from simple to more accurate models

  • Understand recurrent neural networks, its applications and learn how to build these solutions

  • Understand hyper-parameters and tuning

  • Learn how to build industry's leading uses cases eg, Recommendation systems, Speech recognition, commercial grade Image classification and object localization etc....

  • Lead ML/DL projects based on TensorFlow implementation

Available Training Modes

Live Online Training

18 Hours

Classroom Training

 

3 Days
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Who is Deep Learning with TensorFlow Course for?

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What are the prerequisites for Deep Learning with TensorFlow Course?

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Course Outline

  • Introduction and Installation of TensorFlow
    • What is a Tensor
    • What is TensorFlow
    • Installing Anaonda 5.0.1
    • Installing TensorFlow 1.4
    • Installing Keras
    • Getting Started with TensorFlow
    • Code basics
    • Graph visualization
    • Constants, Placeholders, Variables
    • Tensorboard
  • Perceptron
    • Artificial Neuron, comparison to biological Neuron
    • MCP Neuron
    • Perceptron, Adaline and Multi-layer Perceptron
    • Activation functions
    • Various activation functions – Sigmoid, Relu, Tanh, ELU, Softplus...
    • Training rule and Backpropagation
    • Derivatives of activation functions and their relation with backpropagation
    • Hands-on Perceptron
    • Hands-on Gradient Decscent Vs learning rate
    • Hands-on – Activation functions and its analysis
  • Regression with TensorFlow
    • Linear Regression
    • Nonlinear Regression
    • Logistic Regression & Optimization using Loss Function
    • Monitoring using TensorBoard
  • Multi-Layer Perceptrons, Deep Neural Nets, Tuning DNNs
    • Multi-Layer Perceptrons
    • MLP for MNIST classification
    • Hyperparameters and tuning
    • Deep Neural Nets
    • Training challenges and techniques
    • Applying different optimization algorithms
    • Hands-on – Hyperparameters and Tuning
    • L2 regularization
    • Dropouts
    • Building Keras DNN
    • Tensorboard Demo
    • Optimization algorithms
    • Momentum, NAG, Adagrad, RMS prop,
    • Adam optimizer
  • Unsupervised Learning Models
    • Use cases of Unsupervised Learning
    • Restricted Boltzmann Machine : Hands-on
    • Deep Belief Nets
    • Combining unsupervised and supervised training
    • Applications
  • Deep Neural Nets and Designing your Neural Net Solution
    • Feature Extraction
    • The complexity of extracted features and Layers of Neural Nets
    • Working of a Deep Network
    • Training using Backpropagation
    • Types of Deep Networks
    • Hyper-parameters tuning
    • Role of Activation functions in training efficiency and accuracy, More variants of activation functions
  • Convolutional Neural Networks
    • Theory and application of CNN
    • Convolution Layer in detail
    • Pooling Layer Application
    • Font Classification using CNN
    • Hand-written digit recognition using CNN with Keras
    • An overview of pre-trained models (Alex net, VGG net,) and transfer learning
    • Image classification using CNN (commercial grade mage recognition using kaggle dataset)
  • Recurrent Neural Networks (RNN)
    • Theory and application of RNN
    • Long short-term memory
    • Recursive Neural Tensor Theory
    • Recurrent Neural Network Model
    • LSTM cell
    • Predictive Sentiment Analysis using LSTM
    • Word embeddings and lang translation
    • Time Series prediction using RNN
    • Stock price analysis and prediction using RNNs
  • Factorization Models
    • Collaborative Filtering Approaches
    • Recommendation Systems
    • Factorization machines in detail
    • Tffm: FM in TensorFlow
    • Movie Recommendation system implementation using FM
    • Improved Factorization Machines
  • Deep Learning in Audio and Speech processing
    • Audio processing primer
    • Spectrograms, MFC, MFCC
    • CNN's ability to detect spectro Temporal Patterns
    • Audio classification on Urban Sound 8K Dataset using Keras and CNN
    • Connectionist Temporal Classification
    • Hands-on RNN for Speech recognition
  • More on Neural Nets, advancements
    • Generative Adversarial Networks and Applications
    • Building a Noise to Image Generator using GAN
    • Under complete Autoencoders
    • Deep Reinforcement Learning
    • Deep Q-learning

Who is the instructor for this training?

The trainer for this Deep Learning with TensorFlow Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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