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.
 
 
Mastering in Deep Learning
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The 1-on-1 Advantage

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

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

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

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
Online
12 - 16 Jun 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
03 - 07 Jul 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Can’t Attend Live Online? Click to Buy Flexi For Only $199 (Video Recording of Live Online)
Mastering in Deep Learning
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.
 

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Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
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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.

FAQ's

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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.
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