Deep learning(AI) Training

Ducat provides the Best Deep Learning training in Noida based on current industry standards that help attendees to secure placements in their dream jobs at MNCs. Ducat Provides Best Deep Learning Training in Noida. Ducat is one of the most credible Deep Learning training institutes in Noida offering hands-on practical knowledge and full job assistance with basic as well as advanced level Deep Learning training courses. At Ducat Deep Learning training in Noida is conducted by subject specialist corporate professionals with 13+ years of experience in managing real-time A.I. projects. Ducat implements a blend of academic learning and practical sessions to give the student optimum exposure that aids in the transformation of naïve students into thorough professionals that are easily recruited within the industry.

PYTHON DEEP LEARNING USING TENSOR FLOW AND KERAS

Introduction to Neural Network

  • what is neural network..?
  • How neural networks works?
  • Gradient descent
  • Stochastic Gradient descent
  • Perceptron
  • Multilayer Perceptron
  • BackPropagation

Building Deep learning Environment

  • Overview of deep learning
  • DL environment setup locally
    • Installing Tensorflow
    • Installing Keras
  • Setting up a DL environment in the cloud
    • AWS
    • GCP
  • Run Tensorflow program on AWS cloud plateform

Tenserfow Basics

  • Placeholders in Tensorflow
    • Defining placeholders
    • Feeding placeholders with data
  • Variables,
  • Constant
  • Computation graph
  • Visualize graph with Tensor Board

Activation functions

  • What are activation functions?
  • Sigmoid function
  • Hyperbolic Tangent function
  • ReLu -Rectified Linear units
  • Softmax function

Training Neural Network for MNIST dataset

  • Exploring the MNIST dataset
  • Defining the hyperparameters
  • Model definition
  • Building the training loop
  • Overfitting and Underfitting
  • Building Inference

LEARNING

Word Representation Using word2vec

  • Learning word vectors
    • Loading all dependencies
    • Preparing the text corpus
    • defining our word2vec model
    • Training the model
    • Analyzing the model
  • Visualizing the embedding space by plotting the model on tensorboard

Clasifying Images with Convolutional Neural Networks(CNN)

  • Introduction to CNN
  • Train a simple convolutional neural net
  • Pooling layer in CNN
  • Building ,training and evaluating our first CNN
  • Model performance optimization

Popular CNN Model Architectures

  • Introduction to Imagenet
  • LeNet architecture
  • AlexNet architecture
  • VGGNet architecture
  • ResNet architecture

Introduction to Recurrent Neural Networks(RNN)

  • What are Recurrent Neural Networks (RNNs)?
  • Understanding a Recurrent Neuron in Detail
  • Long Short-Term Memory(LSTM)
  • Back propagation Through Time(BPTT)
  • Implementation of RNN in Keras

Sequence-to-Sequence Models for Building Chatbot

HandWritten Digits and letters Classification Using CNN

  • Code Implementation
    • Importing all of the dependencies
    • Defining the hyperparameters
    • Building a simple deep neural network
    • Convolution in keras
    • Pooling
    • Dropout technique
    • Data augmentation
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