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    Deep learning Training India | Deep learning Course India | Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course we will start with deep learning introduction and you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning. Mildaintrainings provides The Best Deep Learning Course by Industry Experts. Enroll & Get Certified now!

    • ✔ Course Duration : 4 Days / 32 Hrs
    • ✔ Training Options : Live Online / Self-Paced / Classroom
    • ✔ Certification Pass : Guaranteed

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    32 hrs

    Course Duration


    Countries And Counting


    Corporates Served

    20+ hrs



    Deep learningtraining india is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course India is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.

    Advancements in deep learning course india are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

    And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.

    What you will Learn

    • Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
    • Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
    • Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
    • Build deep learning models in TensorFlow and interpret the results
    • Understand the language and fundamental concepts of artificial neural networks
    • Troubleshoot and improve deep learning models
    • Build your own deep learning project
    • Differentiate between machine learning, deep learning and artificial intelligence


    no prerequisites required for these course


    Learning Objectives:

    Introduction to deep learning

    • What is a neural network?
    • Supervised Learning with Neural Networks
    • Why is Deep Learning taking off?
    • Introduction to deep learning
    Learning Objectives:

    Neural Networks Basics

    • Binary Classification
    • Logistic Regression
    • Logistic Regression Cost Function
    • Gradient Descent
    • Derivatives
    • Computation graph
    • Derivatives with a Computation Graph
    • Logistic Regression Gradient Descent


    • Vectorizing Logistic Regression
    • Vectorizing Logistic Regression's Gradient Output
    • Broadcasting in Python
    • A note on python/numpy vectors
    • Quick tour of Jupyter/iPython Notebooks
    • Explanation of logistic regression cost function (optional)

    Shallow neural networks

    • Neural Networks Overview
    • Deep Learning Honor Code
    • Logistic Regression with a Neural Network mindset
    • Neural Network Representation
    • Computing a Neural Network's Output
    • Vectorizing across multiple examples
    • Explanation for Vectorized Implementation
    • Activation functions
    • Why do you need non-linear activation functions?
    • Derivatives of activation functions
    • Gradient descent for Neural Networks
    • Backpropagation intuition (optional)
    • Random Initialization
    Learning Objectives:

    Deep Neural Networks

    • Deep L-layer neural network
    • Forward Propagation in a Deep Network
    • Getting your matrix dimensions right
    • Why deep representations?
    • Building blocks of deep neural networks
    • Forward and Backward Propagation
    • Parameters vs. Hyperparameters
    • What does this have to do with the brain?
    • Deep Neural Network – Application

    Key concepts on Deep Neural Networks

    • Building your Deep Neural Network: Step by Step
    • Deep Neural Network - Application
    Learning Objectives:

    Foundations of Convolutional Neural Networks

    • Computer Vision
    • Edge Detection
    • Padding
    • Strided Convolutions
    • Convolutions Over Volume
    • One Layer of a Convolutional Network
    • Simple Convolutional Network Example
    • Pooling Layers
    • Why Convolutions?

    Deep convolutional models: case studies

    Object detection

    • Object Localization
    • Landmark Detection
    • Object Detection
    • Convolutional Implementation of Sliding Windows
    • Bounding Box Predictions
    • Intersection Over Union
    • Non-max Suppression
    • Anchor Boxes
    • YOLO Algorithm

    • Special applications: Face recognition & Neural style transfer

    • What is face recognition?
    • Siamese Network
    • Triplet Loss
    • Face Verification and Binary Classification
    • What is neural style transfer?
    • What are deep ConvNets learning?
    • Cost Function
    • Content Cost Function
    • Style Cost Function
    • 1D and 3D Generalizations
    • Face Recognition for the Happy House
    Learning Objectives:

    Recurrent Neural Networks

    • Why sequence models
    • Notation
    • Recurrent Neural Network Model
    • Back propagation through time
    • Different types of RNNs
    • Language model and sequence generation
    • Sampling novel sequences
    • Vanishing gradients with RNNs
    • Gated Recurrent Unit (GRU)
    • Long Short Term Memory (LSTM)
    • Bidirectional RNN
    • Deep RNNs
    • Building a recurrent neural network - step by step
    • Dinosaur Island - Character-Level Language Modeling
    • Other-Jazz improvisation with LSTM

    Natural Language Processing & Word Embeddings

    • Word Representation
    • Using word embeddings
    • Properties of word embeddings
    • Embedding matrix
    • Learning word embeddings
    • Word2Vec
    • Negative Sampling
    • GloVe word vectors
    • Sentiment Classification
    • Debiasing word embeddings
    • Operations on word vectors – Debiasing

    Sequence models & Attention mechanism

    • Basic Models
    • Picking the most likely sentence
    • Beam Search
    • Refinements to Beam Search
    • Error analysis in beam search
    • Bleu Score (optional)
    • Attention Model Intuition
    • Attention Model
    • Speech recognition
    • Trigger Word Detection
    • Neural Machine Translation with Attention
    • Trigger word detection


    You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
    1. Online ,By deposit the mildain bank account
    2. Pay by cash team training center location

    Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

    Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

    We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

    Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

    Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

    Just give us a CALL at +91 8447121833 OR email at info@mildaintrainings.com



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  • Real-life Case Studies
    Live project based on any of the selected use cases, involving implementation of the various Course concepts.
  • Assignments
    Each class will be followed by practical assignments.
  • Lifetime Access
    You get lifetime access to presentations, quizzes, installation guide & class recordings.
  • 24 x 7 Expert Support
    We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
  • Certification
    Sucessfully complete your final course project and Mildaintrainings will give you Course completion certificate.
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