Mastering in Deep Learning Course Overview

Deep Learning is a subfield of machine learning that utilizes artificial neural networks to model and solve complex problems. It is used in a variety of fields such as computer vision, natural language processing, speech recognition, and robotics. To master in Deep Learning, one should have a strong understanding of:
  • Mathematical concepts: linear algebra, calculus, and probability theory.
  • Neural network architectures: feedforward, Convolutional, Recurrent, and Generative Adversarial Networks (GANs).
  • Hyperparameter tuning: techniques for optimizing the performance of neural networks.
  • Transfer learning: the ability to use pre-trained models on new tasks.
  • Data pre-processing and augmentation: techniques for improving the quality of the input data.
  • Training strategies: supervised, unsupervised, and reinforcement learning.

Target Audience:

  • Data Scientists and Machine Learning Engineers: professionals who are interested in using Deep Learning for solving complex problems in various industries such as healthcare, finance, and transportation.
  • Software Developers: individuals who want to integrate Deep Learning into their software applications.
  • Researchers and Academics: individuals who are interested in advancing the field of Deep Learning through research and experimentation.
  • Entrepreneurs: individuals who are interested in applying Deep Learning to start-ups and creating new products and services.
  • Students: individuals who are interested in pursuing a career in Data Science, Artificial Intelligence, or Machine Learning.

Learning Objectives:

  • Understanding of the fundamental concepts of Deep Learning, including artificial neural networks and their architecture.
  • Ability to design, implement, and train deep neural networks using popular deep learning frameworks such as TensorFlow, PyTorch, and Keras.
  • Knowledge of various types of neural networks such as feedforward, Convolutional, Recurrent, and Generative Adversarial Networks (GANs).
  • Understanding of transfer learning and the ability to fine-tune pre-trained models for new tasks.
  • Knowledge of techniques for data pre-processing and augmentation to improve the quality of input data.
  • Understanding of different training strategies, including supervised, unsupervised, and reinforcement learning.
  • Ability to perform hyperparameter tuning to optimize the performance of deep neural networks.
  • Understanding of the applications of Deep Learning in various industries, such as computer vision, natural language processing, speech recognition, and robotics.
  • Ability to use Deep Learning for solving real-world problems, such as image classification, text generation, and sentiment analysis.
  • Knowledge of the ethical and societal implications of Deep Learning and artificial intelligence.

The 1-on-1 Advantage

Get 1-on-1 session with our expert trainers at a date & time of your convenience.

Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.

4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.

Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

Module 1: Machine Learning Fundamentals
  • Machine Basics basics
  • Linear algebra and Probability
  • ML Supervised Algorithms
  • ML Supervised Algorithms
  • Introducing Google Colab
  • Tensorflow basic syntax
  • Tensorflow Graphs
  • Tensorboard
  • Introduction to Deep Learning
  • What are the Limitations of Machine Learning
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • What is Deep Learning Networks
  • Why Deep Learning Networks
  • How Deep Learning Works
  • Feature Extraction
  • Working of Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks
  • Feed forward neural networks (FNN)
  • Convolutional neural networks (CNN)
  • Recurrent Neural networks (RNN)
  • Generative Adversarial Neural Networks (GAN)
  • Restricted Boltzmann Machine (RBM)
  • Introduction to Perceptron
  • History of Neural networks
  • Activation functions
  • Sigmoid
  • Relu
  • Softmax
  • Leaky Relu
  • Tanh
  • Gradient Descent
  • Learning Rate and tuning
  • Optimization functions
  • Back propagation and chain rule
  • Fully connected layer
  • Cross entropy
  • Weight Initialization
  • 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 is Artificial Neural Networks
  • Machine Learning Vs Artificial Neural Networks
  • History of ANN
  • Building Blocks
  • Network Topology
  • Evaluating the ANN
  • Improving and tuning the ANN
  • Introduction to Convolutional Neural Networks
  • CNN Applications
  • Architecture of a Convolutional Neural Network
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks
  • Intro 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
  • Time Series Forecasting
  • Practical Aspects of Deep Learning
  • Discover and experiment with a variety of different initialization methods, apply L2 regularisation and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
  • Train / Dev / Test sets
  • Bias / Variance
  • Basic Recipe for Machine Learning
  • Regularisation
  • Why Regularization Reduces Overfitting?
  • Dropout Regularisation
  • Understanding Dropout
  • Other Regularization Methods
  • Mini-batch Gradient Descent
  • Understanding Mini-batch Gradient Descent
  • Exponentially Weighted Averages
  • Understanding Exponentially Weighted Averages
  • Bias Correction in Exponentially Weighted Averages
  • Gradient Descent with Momentum
Live Online Training (Duration : 40 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

2000 + If you accept merging of other students. Per Participant & excluding VAT/GST
4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
1750 Per Participant & excluding VAT/GST
12 - 16 Jun
09:00 AM - 05:00 PM CST
(8 Hours/Day)
03 - 07 Jul
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Course Prerequisites
  • Mathematics: a solid understanding of linear algebra, calculus, and probability theory is essential.
  • Programming: proficiency in Python, a programming language commonly used in Deep Learning, is necessary. Familiarity with other programming languages such as C++ and MATLAB may also be beneficial.
  • Statistics: basic knowledge of statistical concepts, such as hypothesis testing, is helpful for understanding and interpreting the results of Deep Learning models.
  • Computer Science: understanding of algorithms and data structures is important for designing and implementing deep neural networks.
  • Machine Learning: a basic understanding of supervised and unsupervised learning algorithms is recommended, as Deep Learning is a subfield of Machine Learning.
  • Software Engineering: experience with software development practices, such as version control and testing, is beneficial for developing deep learning models that are scalable, maintainable, and reusable.
  • Data Science: experience with data pre-processing, cleaning, and visualization is helpful for preparing and analyzing data for Deep Learning models.

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
on Trust Pilot
Student Name Feedback
Nilesh Padbidri
United States
A1. I am very impressed by the thoroughness and completeness of Shifali's knowledge, her commitment to ensuring that I understand every single detail. What is commendable is she did this, when she was keeping unwell. Seldom do you come across people with this level of potential. I am certain that Shifali is meant for much bigger things and I wish her all the best.


No, the published fee includes all applicable taxes.
Yes, we do.
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
Duration of Ultra-Fast Track is 50% of the duration of the Standard Track. Yes(course content is same).
1-on-1 Public - Select your start date. Other students can be merged. 1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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You can request a refund if you do not wish to enroll in the course.
To receive an acknowledgment of your online payment, you should have a valid email address. At the point when you enter your name, Visa, and other data, you have the option of entering your email address. Would it be a good idea for you to decide to enter your email address, confirmation of your payment will be emailed to you.
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We do accept all major credit cards from Visa, Mastercard, American Express, and Discover.
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Yes, we do accept partial payments, you may use one payment method for part of the transaction and another payment method for other parts of the transaction.
Yes, if we have an office in your city.
Yes, we do offer corporate training More details
Yes, we do.
Yes, we also offer weekend classes.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes. You can access courseware for most vendors.
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Buy-Now. Pay-Later option is available using credit card in USA and India only.
You will receive the digital certificate post training completion via learning enhancement tool after registration.
Yes you can.
Yes, we do. For details go to flexi
You can pay through debit/credit card or bank wire transfer.
Dubai, London, Sydney, Singapore, New York, Delhi, Goa, Bangalore, Chennai and Gurugram.
Yes you can request your customer experience manager for the same.
Yes of course. 100% refund if training not upto your satisfaction.

Prices & Payments

Yes of course.
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Travel and Visa

Yes we do after your registration for course.

Food and Beverages



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