Certified Deep
Learning Professional
 
Hands-On Learning: Real-World Applications
in Deep Learning
Expert-Led Courses and Industry-Recognized
Certification

Achieve Professional Certification
in Deep Learning.


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Certified Deep
Learning Professional
Certification
Training Program
 
Start your AI journey
with Deep Learning
Enhance your career today!
Get in Touch With Us
Program Duration
8-14 weeks
Learning Hours
8-12 hours per week
Program Delivery
Instructor Led
Learning Mode
Physical / Remote
The Certified Deep Learning Professional (CDLP)is tailored for individuals aiming to master deep learning techniques and applications.
This program offers in-depth training on neural networks, deep learning frameworks, and advanced model optimization, providing hands-on experience with cutting-edge technologies.
Ideal for data scientists, machine learning engineers, and AI enthusiasts with a foundational understanding of programming and machine learning, this certification is perfect for those looking to elevate their expertise and advance their careers in the field of artificial intelligence.
Core Highlights

Practical Assignments

We provide hands-on assignments that requires practical implementation.

Virtual Coaching Sessions

Online coaching sessions that happen over the phone, via video, or on a web platform.

1 Year Access to LMS

Get access to learning resources upto 1 year of class completion.

Live Project Experience

Hands-on learning and training gives participants the opportunity to experience real world situations.

Online Assessments

Participants can assess reflect on their own learning and their level/skills.

Access to Practice Labs

Access over 200 hours of premium content, including study materials, videos, and coding labs, all delivered through an advanced and intuitive platform.
Program Curriculum
Module 1 - Introduction to Deep Learning

Introduction to Deep Learning

  1. Deep Learning: A revolution in Artificial Intelligence
  2. Limitations of Machine Learning
  3. What is Deep Learning
  4. Advantage of Deep Learning over Machine Learning
  5. 3 Reasons to go for Deep Learning
  6. Real-Life use cases of Deep Learning
  7. Review of Machine Learning
Module 2 - Understanding Neural Networks with TensorFlow

Understanding Neural Networks with TensorFlow

  1. How Deep Learning Works
  2. Activation Functions
  3. Illustration Perceptron
  4. Training a Perceptron
  5. Important parameters of Perceptron
  6. What is TensorFlow
  7. TensorFlow code-basics
  8. Graph Visualization
  9. Constants, Placeholders, Variables
  10. Creating a Model
Module 3 - Deep dive into Neural Networks with TensorFlow

Deep dive into Neural Networks with TensorFlow

  1. Understand limitations of a Single Perceptron
  2. Understand Neural Networks in Detail
  3. Illustrate Multi-Layer Perceptron
  4. Backpropagation – Learning Algorithm
  5. Understand Backpropagation – Using Neural Network Example
  6. MLP Digit-Classifier using TensorFlow
  7. TensorBoard
Module 4 - Master Deep Networks

Master Deep Networks

  1. Why Deep Networks
  2. Why Deep Networks give better accuracy?
  3. Use-Case Implementation on SONAR dataset
  4. Understand How Deep Network Works?
  5. How Backpropagation Works?
  6. Illustrate Forward pass, Backward pass
  7. Different variants of Gradient Descent
  8. Types of Deep Networks
Module 5 - Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN)

  1. Introduction to CNNs
  2. CNNs Application
  3. Architecture of a CNN
  4. Convolution and Pooling layers in a CNN
  5. Understanding and Visualizing a CNN
Module 6 - Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN)

  1. Introduction to RNN Model
  2. Application use cases of RNN
  3. Modelling sequences
  4. Training RNNs with Backpropagation
  5. Long Short-Term memory (LSTM)
  6. Recursive Neural Tensor Network Theory
  7. Recurrent Neural Network Model
Module 7 - Restricted Boltzmann Machine (RBM) and Autoencoders

Restricted Boltzmann Machine (RBM) and Autoencoders

  1. Restricted Boltzmann Machine
  2. Applications of RBM
  3. Collaborative Filtering with RBM
  4. Introduction to Autoencoders
  5. Autoencoders applications
  6. Understanding Autoencoders
Module 8 - Keras API

Keras API

  1. Define Keras
  2. How to compose Models in Keras
  3. Sequential Composition
  4. Functional Composition
  5. Predefined Neural Network Layers
  6. What is Batch Normalization
  7. Saving and Loading a model with Keras
  8. Customizing the Training Process
  9. Using TensorBoard with Keras
  10. Use-Case Implementation with Keras
Module 9 - TFLearn API

TFLearn API

  1. Define TFLearn
  2. Composing Models in TFLearn
  3. Sequential Composition
  4. Functional Composition
  5. Predefined Neural Network Layers
  6. What is Batch Normalization
  7. Saving and Loading a model with TFLearn
  8. Customizing the Training Process
  9. Using TensorBoard with TFLearn
  10. Use-Case Implementation with TFLearn
Module 10 - Artificial Intelligence

Artificial Intelligence

  1. Introduction
  2. Course Outline and Big Picture
  3. Where to get the Code
  4. How to Succeed in this Course
  5. Warmup
Module 11 - Return of the Multi-Armed Bandit

Return of the Multi-Armed Bandit

  1. Section Introduction: The Explore-Exploit Dilemma
  2. Applications of the Explore-Exploit Dilemma
  3. Epsilon-Greedy
  4. Updating a Sample Mean
  5. Designing Your Bandit Program
  6. Comparing Different Epsilons
  7. Optimistic Initial Values
  8. UCB1
  9. Bayesian / Thompson Sampling
  10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1
  11. Nonstationary Bandits
  12. Bandit Summary, Real Data, and Online Learning
Module 12 - High Level Overview of Reinforcement Learning

High Level Overview of Reinforcement Learning

  1. What is Reinforcement Learning?
  2. On Unusual or Unexpected Strategies of RL
  3. Defining Some Terms
Module 13 - Build an Intelligent Tic-Tac-Toe Agent

Build an Intelligent Tic-Tac-Toe Agent

  1. Naive Solution to Tic-Tac-Toe
  2. Components of a Reinforcement Learning System
  3. The Value Function and Your First Reinforcement Learning Algorithm
  4. Tic Tac Toe Code: Outline
  5. Tic Tac Toe Code: Representing States
  6. Tic Tac Toe Code: Enumerating States Recursively
  7. Tic Tac Toe Code: The Environment
  8. Tic Tac Toe Code: The Agent
  9. Tic Tac Toe Code: Main Loop and Demo
Module 14 - Markov Decision Processes

Markov Decision Processes

  1. Gridworld
  2. The Markov Property
  3. Defining and Formalizing the MDP
  4. Future Rewards
  5. Value Function Introduction
  6. Value Functions
  7. Bellman Examples
  8. Optimal Policy and Optimal Value Function
  9. MDP Summary
Module 15 - Dynamic Programming

Dynamic Programming

  1. Intro to Dynamic Programming and Iterative Policy Evaluation
  2. Gridworld in Code
  3. Designing Your RL Program
  4. Iterative Policy Evaluation in Code
  5. Policy Improvement
  6. Value Functions
  7. Policy Iteration in Code
  8. Policy Iteration in Windy Gridworld
  9. Value Iteration
  10. Value Iteration in Code
  11. Dynamic Programming Summary
Module 16 - Monte Carlo

Monte Carlo

  1. Monte Carlo Intro
  2. Monte Carlo Policy Evaluation
  3. Monte Carlo Policy Evaluation in Code
  4. Policy Evaluation in Windy Gridworld
  5. Monte Carlo Control
  6. Monte Carlo Control in Code
  7. Monte Carlo Control without Exploring Starts
  8. Monte Carlo Control without Exploring Starts in Code
  9. Monte Carlo Summary
Module 17 - Temporal Difference Learning

Temporal Difference Learning

  1. Temporal Difference Intro
  2. TD(0) Prediction in Code
  3. SARSA
  4. SARSA in Code
  5. Q Learning
  6. Q Learning in Code
  7. TD Summary
Module 18 - Approximation Methods

Approximation Methods

  1. Approximation Intro
  2. Linear Models for Reinforcement Learning
  3. Features
  4. Monte Carlo Prediction with Approximation
  5. Monte Carlo Prediction with Approximation in Code
  6. TD(0) Semi-Gradient Prediction
  7. Semi-Gradient SARSA
  8. Semi-Gradient SARSA in Code
  9. Course Summary and Next Steps
Module 19 - Stock Trading Project with Reinforcement Learning

Stock Trading Project with Reinforcement Learning

  1. Stock Trading Project Section Introduction
  2. Data and Environment
  3. How to Model Q for Q-Learning
  4. Design of the Program
  5. Code
  6. Stock Trading Project Discussion
Get Professionally Certified
Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).
This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
Be a part of the global network of data science professionals and join the community across sectors.
 
 



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