Deep Learning Course - Training & Certification
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Deep Learning Course - Training & Certification

Reinvent your Data Science career with this transformational course in Deep Learning!

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.


Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

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Module 1- Foundations of Deep Learning

Learning Objectives: Learn about the basics on which Deep Learning has been constructed.

  • Loss function
  • Cross entropy
  • K-nearest neighbour algorithm
  • Minimizing the error - Regression problem

Hands-on: No hands-on

Module 2- Neural Networks Basics

Learning Objectives
Learn the basics of neural networks and understand the biological inspiration behind the same. Learn to use vectorization to speed up your models. Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.Hands-on session on a real-life case study


  • What is Neural Network?
  • The Biological Inspiration
  • Multilayer Perceptrons
  • Gradient Descent
  • Vectorization
  • Shallow Neural Networks
  • Activation Functions
  • Back Propagation Algorithm
  • Deep L-layer neural network
  • Forward Propagation in a Deep Network
  • Case Study: Neural Networks

Hands-on: The dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph and compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!
Using Base Neural Network and Neural Network with Hidden layers, Activation function, Solver and Learning Rate - Predict close value of stock

Module 3- Introduction to Deep Learning

Learning Objectives:
Understand industry best-practices for building deep learning applications. Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking. Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.Learn Keras for Classification and Regression in Typical Data Science Problems. Learn about  different layers in KERAS and set it up. Hands-on session on a real-life case study


  • Hyperparameters tuning
  • Batch Normalization
  • Optimization algorithms
  • Deep Learning frameworks
  • Weight initialization
  • Deep Learning architecture
  • Introducing Keras
  • Artificial Neural Networks (ANN)
  • Case Study: Artificial Neural Networks (ANN)

Hands-on: Apply Deep Learning framework - Keras to create a Neural Network and train models and monitor the same.The research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients.

Module 4-
Computer Vision

Learning Objectives:Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.

  • Convolutional Neural Networks (CNN)
  • Building blocks of CNN
  • Image Processing using CNN
  • Pre processing and semantic segmentation
  • Object localization and detection
  • Introducing Tensorflow
  • Case Study: Convolutional Neural Networks (CNN) using TensorFlow

    Hands-on: No hands-on

Module 5- Object Detection

Learning Objectives: Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.


  • Object localization
  • Object detection
  • Feature Extraction

    Hands-on: No hands-on

Module 6-TensorFlow

Learning Objectives: Get introduced to TensorFlow, a library. Learn to build a Neural Networks using Tensorflow. Hands-on session on a real-life case study


  • Introducing Tensorflow
  • Case Study: Convolutional Neural Networks (CNN) using TensorFlow

    Hands-on: Apply Deep Learning framework - TensorFlow to create a Neural Network and train models and monitor the same. Handwriting digit recognition using CNN with TensorFlow. This project will help build a model using Convolutional Neural Network to recognize handwriting.

Module 7- Sequence Models

Learning Objectives: Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Hands-on session on a real-life case study.


  • Recurrent Neural Networks (RNN)
  • Backpropagation through time
  • Different types of RNNs
  • Language model and sequence generation
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs
  • Case Study: Recurrent Neural Networks (RNN)

    A time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Using Long-Short-Term-Memory (LSTM) build a time series model to forecast the future values

Module 8- Natural Language Processing (NLP)

Learning Objectives: Learn to use word vector representations and embed layers to train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation. Hands-on session on a real-life case study.


  • Syntax and Parsing Techniques
  • Statistical NLP and text similarities
  • Text summarization techniques
  • Real-Life Case Study

    Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of studying the market perceptions found its way in different social networking platforms such as Twitter. With proper tools and the help of technology, meaningful and precious information can be gathered, analyzed, and utilized in different areas like in the movement and performance of the stock market.

Covers Artificial Neural Networks, Convolutional Neural Networks, Long Short Term Memory, Natural Language Processing

Key Features

40 hours of Instructor led Training
Comprehensive Hands-on with Python
Covers Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
Gain knowledge in Computer Vision applications
Covers TensorFlow and Keras for Deep Learning applications

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

Deep learning has now found uses in every sector to make customer experience better and improve the quality of life. From translation to language recognition and autonomous vehicles to text generation, there are many uses of Deep Learning. Google, Apple, and Toyota are just some of the companies that have spent billions of dollars in developing Deep Learning research and products.
This trend has made deep learning enthusiasts among the most sought after professionals and this is a good workshop for you to master these skills and become proficient in deep learning concepts. The 5th-annual Burtch Works Study: Salaries of Data Scientists puts median compensations for individual contributors in a range of $95,000 at level 1 (0-3 years of experience) to $165,000 at level 3 (9+ years). Managers can earn $145,000 at level 1 (1-3 reports) to $250,000 at level 3 (10+ reports). So, this is the right time to invest in a career in Deep Learning.

You will gain these skills:

Learn about Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks
Be proficient in using TensorFlow and Keras
Get an understanding of Computer Vision applications
Get to know about libraries in Python used in Deep Learning


By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as data analysts.

There are no restrictions but participants would benefit if they have Python programming knowledge and familiarity with Data Science.

Yes, KnowledgeHut offers this training online.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Deep Learning experts who have years of industry experience.

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details:

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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