Deep Learning Specialization Training

Deep Learning Specialization Training

This comprehensive course provides the knowledge and skills to deploy deep learning tools using AI/ML frameworks effectively. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the distinctions between deep learning and machine learning. The course covers a wide range of topics, including neural networks, forward and backward propagation, TensorFlow 2, Keras, performance optimization techniques, model interpretability, Convolutional Neural Networks (CNNs), transfer learning, object detection, Recurrent Neural Networks (RNNs), autoencoders, and creating neural networks in PyTorch. 

By the end of the course, you will have a solid foundation in deep learning principles and the ability to build and optimize deep learning models effectively using Keras and TensorFlow.

Key Features

• Course and material in English 
• Intermediate - advanced level
• 1 year access to the platform & class recordings

• 6 hours of video lessons
• 40 hours online live class 
• 50 hours of study time recommendation 
• 3 course-end project
• Virtual Lab included to practice
• 1 Assessment test
• No exam but certification of completion included

Bonus: In addition to this hands-on e-learning course, we offer you free access to our online classroom sessions whenever its available (every 2-3 months) in addition to your e-learning if you wish. You will have the opportunity to have interaction with the trainer and other participants. These online classroom sessions will also be recorded, so that you can keep the recording for 1 year.

Learning Outcomes:

• Differentiate between deep learning and machine learning and understand their respective applications.
• Gain a thorough understanding of various types of neural networks.
• Master the concepts of forward propagation and backward propagation in Deep Neural Networks (DNNs).
• Gain insight into modeling techniques and performance improvement in deep learning.
• Understand the principles of hyperparameter tuning and model interpretability. 
• Learn about essential techniques such as dropout and early stopping and implement them effectively.
• Develop expertise in Convolutional Neural Networks (CNNs) and object detection.
• Acquire a solid understanding of Recurrent Neural Networks (RNNs).
• Gain familiarity with PyTorch and learn how to create neural networks using this framework.

Target Audience

• Software Engineers & Developers – Those looking to integrate AI and deep learning into their projects.
• Data Scientists & Analysts – Professionals wanting to expand their skill set in neural networks and machine learning.
• AI/ML Enthusiasts – Individuals with a passion for artificial intelligence who want to build real-world applications.
• Students & Researchers – Graduate or undergraduate students in computer science, mathematics, or related fields.
• IT & Cloud Professionals – Those working in cloud computing, DevOps, or infrastructure who need to understand AI models.
• Business & Product Managers – Professionals who need AI knowledge to make data-driven decisions and develop AI-powered products.
• Entrepreneurs & Startups – Innovators aiming to build AI-driven businesses or enhance existing products with deep learning.

Prerequisites: 

Basic Python programming, knowledge of linear algebra, probability, and some machine learning fundamentals are highly recommended.

Topics covered:

Introduction to Deep Learning

• Brief history of AI 
• Motivation for deep learning 
• Difference between Deep Learning and Machine Learning 
• Deep Learning Successes
• Applications of Deep Learning 
• Challenges of deep learning 
• Deep learning frameworks 
• Full cycle of deep learning project 
• Neural Networks and Types of Neural Network 

Perceptron

• Forward propagation in Perceptron 
• Role of Activation functions
• Backward propagation in perceptron
• Gradient descent algorithm
• Limitations of perceptron

Deep Neural Networks

• What is DNN and why it is useful 
• Loss Functions
• Forward Propagation in DNN 
• Backward Propagation in DNN 
• Introduction to TensorFlow
• Training DNN using TensorFlow 
• Introduction to TensorFlow Playground 

TensorFlow

• Introduction to Tensors 
• Sequential APIs in TensorFlow 
• Keras an Introduction  

Model Optimization and Performance Improvement

• Introduction to optimization algorithms 
• Introduction to SGD and implementation
• Introduction to Momentum and implementation 
• Introduction to Adagrad and implementation
• Introduction to Adadelta and implementation 
• Introduction to RMSProp and implementation 
• Introduction to Adam and implementation
• Batch Normalization implementation 
• Exploding and Vanishing Gradients 
• Introduction to Hyperparameter Tuning and implementation
• Model Interpretability 
• Dropout and Early Stopping 

Convolutional Neural Networks

• What is CNN 
• CNN Architecture 
• ResNet 50 
• Working of CNN 
• Pooling in CNN 
• Image Classification using CNN 
• Introduction to Tensorboard 

Transfer Learning

• Introduction to transfer learning 
• How to select pre trained models 
• Advantages of transfer learning 

Object Detection

• Object detection for multiple objects 
• High level overview of YOLO V3 Algorithm 
• Dataset preparation for YOLO V3 Algorithm 
• Object deception with YOLO V3 
• Introduction to TF Lite 
• Converting TF Model into TF Lite Model 

Recurrent Neural Networks (RNNs)

• What is sequence modeling 
• Introduction to RNN
• Architecture of RNN 
• Forward and Back Propagation in RNN 
• Introduction to Hybrid Modeling 
• Architecture of a CNN and RNN hybrid model 

Transformer Models for Natural Language Processing (NLP)

• Overview of transformer models 
• Architecture of the transformer model 
• Introduction to BERT Model

Getting Started with Autoencoders

• Introduction to unsupervised deep learning 
• What are autoencoders
• Architecture of autoencoders
• Use cases and training of autoencoders

PyTorch

• Getting stated with PyTorch
• Creating a Neural Network in Pytorch

Will missing a live class affect my ability to complete the course?

No, missing a live class will not affect your ability to complete the course. With our 'flexi-learn' feature, you can watch the recorded session of any missed class at your convenience. This allows you to stay up-to-date with the course content and meet the necessary requirements to progress and earn your certificate. Simply visit the learning platform, select the missed class, and watch the recording to have your attendance marked.

Licensing and accreditation

This course is offered by Simplilearn according to Partner Program Agreement and complies with the License Agreement requirements. AVC Promotes this course on behalf of Simplilearn.

Equity Policy

Simplilearn does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.