Description

This Deep Learning with TensorFlow course focuses on TensorFlow.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

TensorFlow is counted as one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It is being widely used to develop solutions with Deep Learning.

 

Radiant Techlearning offers “Deep Learning using Tensor Flow Using Python Certification TOC” training program in Classroom & Virtual Instructor Led / Online mode.

Pre-requisites

  • Basic Knowledge of Deep Learning

Course Content

Module 1: Machine Learning – An Introduction

  • What is machine learning?
  • Different machine learning approaches
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Steps Involved in machine learning systems

Module 2: Brief description of popular techniques/algorithms

  • Linear regression
  • Decision trees
  • K-means
  • Naïve Bayes
  • Support vector machines
  • The cross-entropy method

Module 3: Introduction to Deep Learning and Neural Networks

  • Why neural networks?
  • What is Deep learning
  • Applications in real life
  • Applications in industry like Signal processing, Medical, Autonomous car driving etc
  • A popular open source package

Module 4: Neural Networks

  • Neurons and layers
  • Different types of activation function
  • The back-propagation algorithm
  • Linear regression
  • Logistic regression

Module 5: Deep Learning Fundamentals

  • What is deep learning?
  • Fundamental concepts
  • Feature learning
  • Deep learning algorithms
  • Deep learning applications
  • Speech recognition
  • Object recognition and classification
  • GPU versus CP
  • Popular open source libraries – Theano
  • TensorFlow
  • Keras
  • Sample deep neural net code using Keras

Module 6: 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

Module 7: Recurrent Neural Networks and Language Models

  • Introduction 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

Module 8: Restricted Boltzmann Machine and Auto encoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders

Module 9: Keras API

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

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