Certificate in Deep Learning with TensorFlow

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive Online e-Learning Access (LMS)
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification

$49.00 /-
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Deep Learning uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation on large volumes of data in order to make decisions about high dimensional data. 

Note: Hard copy material is not applicable for this course.

Why should one take this certification?

In this certification course, You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Engine.

You’ll also learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras.

Who will benefit from taking this certification?

Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in Deep learning with TensorFlow framework benefits Data Science professionals, students and professionals across various Industries.

Deep Learning with TensorFlow Table of Contents


Deep Learning with TensorFlow Interview Questions


Deep Learning with TensorFlow Practice Test


Companies that hire Deep Learning with TensorFlow Professionals

IT companies, MNCs, Consultancies hire Data Science professionals for Data Science activities. Companies employing Data Science include Capgemini, JP Morgan Chase, TCS, Wipro, Zensar, Accenture, Infor etc.

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  • Software Installation

Keras Introduction

  • Introduction
  • Keras Backends
  • Design and Compile a Model
  • Model Training, Evaluation, and Prediction
  • Training with Data Augmentation
  • Training with Transfer Learning and Data Augmentation

Scaling Deep Learning Using Keras and Tensorflow

  • Introduction to TensorFlow
  • Introduction to TensorBoard
  • Types of Parallelism in Deep Learning – Synchronous and Asynchronous
  • Distributed TensorFlow
  • Configuring Keras to use TensorFlow for Distributed Problems

Training, Tuning, and Serving Our Model in the Cloud

  • Introduction
  • Introduction to Google Cloud Machine Learning Engine
  • Datasets, Feature Columns, and Estimators
  • Representing Data in TensorFlow
  • Quick Dive into TensorFlow Estimators
  • Creating Data Input Pipelines
  • Setting Up Our Estimator
  • Packaging Our Model
  • Training with Google Cloud ML Engine
  • Hyperparameter Tuning in the Cloud
  • Deploying Our Model for Prediction
  • Creating Our Prediction API

Setting Up the Deep Learning Playground

  • TensorFlow for Building Deep Learning Models
  • Basic Syntaxes, Function Optimization, Variables, and Placeholders
  • TensorBoard for Visualization

Training Deep Feed-Forward Neural Networks with Tensorflow

  • Start by Loading the Imported Dataset
  • Building the Layers of the Neural Network in TensorFlow
  • Optimizing the Softmax Cross Entropy Function
  • Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not

Applying CNN on Two Real Datasets

  • Importing the Two Datasets Using TensorFlow and Sklearn API
  • Writing the TensorFlow Code to Add Convolutional and Pooling Layers
  • Using tf.train.AdamOptimizer API to Optimize CNN
  • Implementing CNN to Create a Face Recognition System

Exercise Rnn to Solve Two Time Series Problems

  • Understanding the RNN and the Need for LSTM
  • Implementing RNN
  • Monthly Riverflow Prediction of Turtle River in Ontario
  • Implement LSTM Project to Predict Decimal Number of Given Binary Representation

Using Autoencoders to Efficiently Represent Data

  • Encoder and Decoder for Efficient Data Representation
  • TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
  • Using Stacked Autoencoders for Representation on MNIST Dataset
  • Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset

Generative Adversarial Networks for Creating Synthetic Dataset

  • Generative Adversarial Networks for Creating Synthetic Dataset
  • Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
  • Coding the Generator and Discriminator Using TensorFlow
  • Training GANs to Create Synthetic GPS Based Trajectories

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