Deep Learning Specialization

2Month/20 Hour                                                   Price:125,000

                                                                                      120,000 

Deep Learning Specialization

The Deep Learning Specialization is designed to equip learners with the advanced skills required to develop deep learning models and apply them to real-world problems. This course covers the foundational aspects of deep learning, including neural networks, convolutional networks, recurrent networks, and techniques to improve model performance. Students will work on hands-on projects to gain practical experience with cutting-edge tools and frameworks like TensorFlow and Keras.

Course Objectives:

Understand the fundamentals of deep learning and neural networks
Build and train neural networks for various tasks using TensorFlow and Keras
Develop expertise in convolutional and recurrent neural networks
Implement techniques to optimize deep learning models (regularization, dropout, etc.)
Apply deep learning to real-world applications such as image classification, language processing, and time series analysis

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Course Content

Module 1: Introduction to Deep Learning 

What is Deep Learning?

Overview of deep learning and how it differs from traditional machine learning
The importance of neural networks in modern AI

Neural Networks Basics:

Structure of a neural network: Neurons, layers, and activation functions
Forward and backward propagation
Loss functions and optimization techniques (Gradient Descent, Stochastic Gradient Descent)

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Module 2: Neural Networks and Optimization 

 

Training Deep Networks:
Supervised learning and backpropagation in neural networks
Cost functions, vanishing gradients, and exploding gradients
Weight initialization and optimization methods (Adam, RMSprop)

Improving Model Performance:
Regularization techniques: L2 and L1 regularization, dropout, batch normalization
Understanding and addressing bias-variance trade-off

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Module 3: Convolutional Neural Networks (CNNs) for Image Processing 

What are CNNs?

The architecture of CNNs: Convolutions, pooling, and fully connected layers
Understanding filters, kernels, and feature maps

Building CNNs:How to build CNNs for image classification tasks
Transfer learning and pre-trained CNN architectures (e.g., VGG, ResNet)

Object Detection with CNNs:
Techniques like YOLO and Faster R-CNN for object detectionning

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Module 4: Recurrent Neural Networks (RNNs) for Sequential Data 

Understanding RNNs:

Why RNNs are important for sequential data
Architecture of RNNs: Unfolding the network and handling time steps

Advanced RNN Techniques:

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
Addressing vanishing gradients in RNNs

Applications of RNNs:
Time series forecasting, text generation, language translation, and speech recognition

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Module 5: Unsupervised Learning with Autoencoders and GANs 

 

Autoencoders

 

Architecture of autoencoders for unsupervised learning
Applications of autoencoders in dimensionality reduction and anomaly detection

Generative Adversarial Networks (GANs)

Overview of GANs and their architecture
Training GANs: The generator and discriminator networks
Applications of GANs in image generation, style transfer, and creative AI

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Module 6: Deep Learning Frameworks and Model Deployment (5 Hours)

Working with TensorFlow and Keras:

Overview of TensorFlow’s architecture and building deep learning models with Keras API
Model training, evaluation, and fine-tuning with TensorFlow

Model Deployment:

Strategies for deploying deep learning models in production (cloud-based deployment)
Introduction to TensorFlow Serving and ONNX for cross-framework deployments

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Module 7: Advanced Techniques for Deep Learning 

Reinforcement Learning Basics:Understanding the key concepts of reinforcement learning
Applications of deep reinforcement learning in gaming, robotics, and more

Attention Mechanisms:Overview of attention in deep learning, particularly in NLP tasks
How attention enhances the performance of models like transformers

Model Interpretability:
Techniques for interpreting and explaining deep learning models

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Career Path After Completion:

Upon completion of this Deep Learning Specialization, participants can pursue careers in:

AI Engineer: Develop, train, and optimize deep learning models for various applications.

Machine Learning Engineer: Specialize in building scalable deep learning solutions using TensorFlow and Keras.

Data Scientist (Deep Learning): Leverage deep learning to solve complex problems in areas like computer vision, natural language processing, and more.

AI Researcher: Advanced research in the fields of neural networks, reinforcement learning, and unsupervised learning.

AI Product Developer: Use deep learning to create innovative AI-powered products, including autonomous systems, recommendation engines, and chatbots.

Course Prerequisites:

Basic understanding of machine learning algorithms
Familiarity with Python programming
Knowledge of linear algebra, calculus, and probability is recommended
Prior experience with any deep learning framework is beneficial but not required

International Student Fees: USD525

Job Interview Preparation  (Soft Skills Questions & Answers)

Tough Open-Ended Job Interview Questions
What to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview

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Flexible Class Options

Week End Classes For Professionals  SAT | SUN
Corporate Group Training Available
Online Classes – Live Virtual Class (L.V.C), Online Training

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