Deep Learning: Beginner to Advanced with Generative AI

About Course

Dive into the world of deep learning with our comprehensive course designed for both beginners and advanced learners. This course will take you through the fundamentals of deep learning, advanced techniques, and the exciting field of generative AI. Learn to build, train, and deploy deep learning models with practical applications and real-world projects.

Key Features:

Comprehensive Curriculum: From basic to advanced deep learning and generative AI techniques.
Expert Instructor: Learn from an industry expert with extensive experience in AI.
Hands-On Projects: Practical assignments and projects to reinforce learning.
Interactive Quizzes: Regular quizzes to test understanding and reinforce key concepts.
Capstone Project: An end-to-end project integrating all learned skills.
Career Preparation: Modules on resume building, LinkedIn optimization, and mock interviews.
Lifetime Access: Revisit and review content anytime with lifetime access.

Fundamental and advanced concepts of deep learning
Building and training neural networks using TensorFlow and Keras
Implementing CNNs, RNNs, and advanced architectures like ResNet and VGG16
Developing generative models like GANs and VAEs
Practical applications of generative AI
Neural Architecture Search techniques: ENAS and PNAS
Preparing for a career in deep learning and AI

Course Content

Introduction to Deep Learning
Introduction to Deep Learning

Overview of deep learning and its applications
Understanding neural networks and their components
Setting up the deep learning environment
Introduction to TensorFlow and Keras
Deep Learning Basics
Simple neural network in TensorFlow

Neural Networks and Forward Propagation
Neural Networks and Forward Propagation

Training Neural Networks
Training Neural Networks

Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs)

Advanced Deep Learning Techniques
Advanced Deep Learning Techniques

ResNet Architecture
ResNet Architecture

VGG16 Architecture
VGG16 Architecture

Neural Architecture Search (NAS)
Neural Architecture Search (NAS)

Efficient Neural Architecture Search (ENAS)
Efficient Neural Architecture Search (ENAS)

Progressive Neural Architecture Search (PNAS)
Progressive Neural Architecture Search (PNAS)

Introduction to Generative AI
Introduction to Generative AI

Advanced Generative Models
Advanced Generative Models

Practical Applications of Generative AI
Practical Applications of Generative AI

Sequence Modeling
Sequence Modeling

Attention Mechanisms and Transformers
Attention Mechanisms and Transformers

Transfer Learning and Fine-Tuning
Transfer Learning and Fine-Tuning

Model Deployment
Model Deployment

Real-World Deep Learning Projects
Real-World Deep Learning Projects

Preparing for Job Success
Preparing for Job Success

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About Course

What Will You Learn?

Course Content

Introduction to Deep Learning Introduction to Deep Learning

Overview of deep learning and its applications

Understanding neural networks and their components

Setting up the deep learning environment

Introduction to TensorFlow and Keras

Deep Learning Basics

Simple neural network in TensorFlow

Neural Networks and Forward Propagation Neural Networks and Forward Propagation

Detailed anatomy of neural networks

Activation functions and their roles

Forward propagation and cost function

Implementing a basic neural network from scratch

Neural Network Fundamentals

Build and train a neural network

Training Neural Networks Training Neural Networks

Gradient descent and backpropagation

Optimizers: SGD, Adam, RMSprop

Loss functions and their importance

Avoiding overfitting: regularization techniques

Training Techniques

Implement backpropagation and train a network

Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNNs)

Introduction to CNNs and their applications

Convolutional layers, pooling layers, and fully connected layers

Building a CNN for image classification

Transfer learning with pre-trained models

CNN Concepts

Build and train a CNN on a custom dataset

Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs)

Understanding sequential data and RNNs

LSTM and GRU architectures

Applications of RNNs in text and time-series data

Implementing an RNN for text generation

RNN Fundamentals

Build and train an RNN for a specific task

Advanced Deep Learning Techniques Advanced Deep Learning Techniques

Introduction to advanced architectures: ResNet, Inception, etc.

Autoencoders and their applications

Attention mechanisms and Transformers

Practical implementation of advanced models

Advanced Techniques

Experiment with an advanced architecture

ResNet Architecture ResNet Architecture

Introduction to ResNet (Residual Networks)

Understanding residual learning and skip connections

Implementing ResNet for image classification

Comparing ResNet with traditional CNNs

ResNet Concepts

Implement ResNet on a custom dataset

VGG16 Architecture VGG16 Architecture

Introduction to VGG16

Detailed architecture and design choices

Building VGG16 from scratch

Transfer learning with VGG16

VGG16 Fundamentals

Implement VGG16 for a specific task

Neural Architecture Search (NAS) Neural Architecture Search (NAS)

Introduction to NAS

Overview of ENAS (Efficient NAS) and PNAS (Progressive NAS)

Implementing a simple NAS

Advantages and challenges of NAS

NAS Concepts

Experiment with NAS techniques

Efficient Neural Architecture Search (ENAS) Efficient Neural Architecture Search (ENAS)

Detailed understanding of ENAS

Implementing ENAS for model optimization

Case studies and practical applications

ENAS Techniques

Implement ENAS on a specific task

Progressive Neural Architecture Search (PNAS) Progressive Neural Architecture Search (PNAS)

Detailed understanding of PNAS

Implementing PNAS for model optimization

Case studies and practical applications

PNAS Techniques

Implement PNAS on a specific task

Introduction to Generative AI Introduction to Generative AI

Introduction to Generative AI

Introduction to Deep Learning
Introduction to Deep Learning

Neural Networks and Forward Propagation
Neural Networks and Forward Propagation

Training Neural Networks
Training Neural Networks

Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs)

Advanced Deep Learning Techniques
Advanced Deep Learning Techniques

ResNet Architecture
ResNet Architecture

VGG16 Architecture
VGG16 Architecture

Neural Architecture Search (NAS)
Neural Architecture Search (NAS)

Efficient Neural Architecture Search (ENAS)
Efficient Neural Architecture Search (ENAS)

Progressive Neural Architecture Search (PNAS)
Progressive Neural Architecture Search (PNAS)

Introduction to Generative AI
Introduction to Generative AI

Advanced Generative Models
Advanced Generative Models

Practical Applications of Generative AI
Practical Applications of Generative AI

Sequence Modeling
Sequence Modeling

Attention Mechanisms and Transformers
Attention Mechanisms and Transformers

Transfer Learning and Fine-Tuning
Transfer Learning and Fine-Tuning

Model Deployment
Model Deployment

Real-World Deep Learning Projects
Real-World Deep Learning Projects

Preparing for Job Success
Preparing for Job Success