Advanced Certification Program in Generative AI & Deep Learning

Program Overview

In the era of artificial intelligence, Generative AI and Deep Learning are reshaping industries, fueling innovation, and enabling revolutionary breakthroughs. The Professional Certification Program in Generative AI & Deep Learning is tailored to empower professionals and enthusiasts with cutting-edge expertise to excel in these transformative domains.

Why Pursue a Professional Certification in Generative AI & Deep Learning?

Generative AI, combined with the power of Deep Learning, is revolutionizing fields like healthcare, finance, entertainment, and autonomous systems. This certification program offers a well-structured and practical learning path, equipping you with the tools and knowledge to master advanced AI models, such as Generative Adversarial Networks (GANs) and Transformer-based architectures like GPT and BERT.

Program Highlights of the Professional Certification Program in Generative AI & Deep Learning
1. Comprehensive Curriculum

This program covers foundational and advanced concepts, including:

  • Deep Learning Fundamentals: Neural networks, backpropagation, and optimization techniques
  • Generative AI Models: GANs, Variational Autoencoders (VAEs), and Diffusion Models
  • Transformer Architectures: GPT, BERT, and applications in NLP
  • Reinforcement Learning: Integration with generative models
  • AI Ethics: Responsible AI practices and bias mitigation
  • Real-World Applications: Implementing generative AI in image generation, natural language processing, and beyond
2. Learn from Industry Experts

Gain insights from leading AI researchers and industry professionals:

  • Live interactive classes led by experienced instructors
  • Hands-on workshops using advanced tools and libraries like TensorFlow, PyTorch, and Hugging Face
  • Continuous mentorship from domain experts
3. Practical, Hands-On Learning

Apply your knowledge with real-world projects and assignments:

  • Work on capstone projects, such as building chatbots, creating deepfake detectors, or generating synthetic datasets
  • Develop deployable AI models with end-to-end pipelines
  • Receive personalized feedback to refine your skills
4. Networking & Collaboration
  • Collaborate with a diverse community of learners, mentors, and industry leaders
  • Participate in virtual hackathons and innovation challenges
  • Build lasting professional connections in the AI ecosystem
5. Flexible Learning Support
  • 1:1 Personalized Doubt Clearing through video calls, email, and chat
  • Self-paced modules to accommodate your schedule
  • Dedicated program managers for continuous guidance
6. Globally Recognized Certification

Earn a Professional Certification in Generative AI & Deep Learning backed by industry leaders. Showcase your expertise to employers worldwide and stay competitive in the ever-evolving AI job market.

Why Choose This Program?
  • Learn the latest advancements in Generative AI and Deep Learning
  • Gain job-ready skills with practical, project-based learning
  • Access lifetime career support, including resume building, interview preparation, and placement assistance
  • Unlock career opportunities in high-demand fields such as AI research, robotics, gaming, and more
Take the Leap into the Future of AI

Empower yourself with advanced skills in Generative AI and Deep Learning and lead innovation in your field. Join Digicrome’s Professional Certification Program to build a career at the forefront of artificial intelligence.

📌 Apply Now to transform your career and shape the future of AI. Don't miss this opportunity to stay ahead in the rapidly evolving world of technology!

Digicrome has meticulously crafted this Job-Ready Certification Program to give your career the boost it deserves. With a focus on practical learning, industry-recognized credentials, and real-world applications, this program is designed to equip you with the skills and confidence to achieve unparalleled career growth.

Advanced Certification Program in Generative AI & Deep Learning

  1. 172500.00
Features
  • 06-Months Live Online Program
  • Placement Readiness Program
  • AI, Projects and Case Studies
  • Topic Wise Case Study Provide

Key Highlights

  • Live Online06-Months Live Online Program
  • Experienced FacultiesPlacement Readiness Program
  • Students HandoutsAI, Projects and Case Studies
  • Solid FoundationTopic Wise Case Study Provide
  • Solid FoundationLatest Tool & Technology Covered
  • Solid FoundationFlexible Learning Modes
  • Solid FoundationLifetime LMS Support
  • Solid Foundation1:1 Mentorship Provide

Program Objective

1.1 What is ML, Why ML, Types of ML, (Training, Validation, and Testing Set)

1.2 Train/Test Split, Preprocessing of Data (LabelEncoder, OneHotEncoder), Standardization of Data

1.3 Hyperparameters, Selection and Fine Tuning of Models, (Main Challenges - Overfitting, Underfitting, Poor Quality Data, Irrelavant Features, etc.) 

2.1 Descriptive Statistics - Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers), Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom), Skewness and Kurtosis

2.2 Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling

2.3 Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution

2.4 Correlation Coffecient, Coefficient of Determination, Simple Linear Regression in Statistics 

3.1 Performance Metrics - Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve

3.2 Classification Models - Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Decission Trees

3.3 Ensembling Methods - Bagging (Voting Classifer, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random Forest Classifer, Stacking

3.4 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and L2 Regularization for overfitting, Handling Class Imabalance

3.5 Classification Project - Real World Use Case 

4.1 Introduction - Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient Descent

4.2 Performance Metrics - Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.

4.3 Challenges - Heteroskedasticity, Non - Normality of Data, Multicollinearity of Data, etc.

4.4 Regression Models - Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)

4.5 Ensemble Models - Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods

4.6 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization

4.7 Regression Project - Real World Use Case

4.1 Introduction to Unsupervised Learning

4.2 Clustering Methods - KMeans, Hierarchical, Model Based Clustering, DBSCAN Clustering, Anamoly Detection using Gaussian Mixture Models

4.3 Dimensionality Reduction using Principal Component Analysis

4.4 Building and Working of Recommendation Engines

 Basic Concepts

1.1 Biological to Artificial Neurons

1.2 The perceptron

1.3 Multi-layer Perceptrons (MLPs)

1.4 Input Layer, Hidden Layers and Output layers

1.5 Weights and Biases

1.6 Regression MLPs

1.7 Classification MLPs

1.8 Activation functions and Optimizers

2.1 Building a Neural Network using Sequential API

2.2 Building a Neural Network using Functional API

2.3 Building a Neural Network using Subclassing API

2.4 Saving and Restoring a Model

2.5 Callbacks  

3.1 Vanishing/Exploding Gradients Problem

3.2 Batch Normalization 3.3 Gradient Clipping

3.4 Transfer Learning - Using Pretrained Layers

3.5 Pretraining on Auxiliary Task

3.6 Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient

3.7 Learning Rate Scheduling  

4.1 How to choose number of hidden layers and number of Neurons

4.2 Learning Rate, Optimizer, Batch Size, Loss Functions and Activation Functions

4.3 L1 and L2 Regularization

4.4 Dropouts and Batch Normalization

4.5 Max Norm Regularization 

 Convolutional Neural Networks (CNNs)

1.1 Structure - How CNNs are different from Traditional Neural Networks

1.2 Building Blocks - Filters, Kernals, Feature Maps, Pooling (Max, Average, Global), Padding (Valid vs Same)

1.3 Architectural Designs for Generative AI - Transposed Convolutions, Unsampling Techniques, Residual Connections (Skip Connections)

1.4 Types of CNNs in Generative AI - Encoder-Decoder, UNet, VGG and ResNet Variants, Dilated Convolutions, MultiScale Convolutions, Attention Mechanisms, Conditional CNNs

1.5 Relevance - High Resolution Image Generation, Image Synthesis, Texture Synthesis, Video Generation 

2.1 Core Concepts - Hidden State, Back Propagation through time, Challenges (Vanishing/Exploding Gradients, Short term Memory)

2.2 Basic Architectures (Simple and Deep RNNs), Advanced Architectures (Long Short-Term Memory, Gated Recurrent Units), Bidirectional RNNs, Sequence to Sequence Models)

2.3 RNN Variants for Generative AI - Attention Mechanisms in RNNs, Conditional RNNs, Hierarchical RNNs)

2.4 Incorporate Transformers, Hybrid Models (Combination of RNNs with CNNs and Attention Mechanisms for Generative AI)

2.5 Applications - Text Generation, Music Compostion, Speech Synthesis, Video Generation, Language Translation

3.1 Architecture - Encoder/Decoder Structure, Self Attention Mechanism, Positional Encoding, Residual Connections, Training of Transformer Models

3.2 Variants of Transformers - Encoder Only, Decoder Only, Encoder-Decoder, Vision Transformers, Multimodel Transformers, Efficient Transformers

3.3 Attention Mechanisms - Soft, Hard, Sparse, and Cross Attention Mechanisms

3.4 Fine Tuning and Transfer Learning - Prompt Engineering, Few-shot and Zero-shot Learning, LoRA (Low Rank Adaptation)

3.5 Transformer Models for Text Generation - BERT, GPT (2,3,4), BART, CLIP

3.6 Relevance for Generative AI - Autoregressive Modelling, Masked Language Modelling, Sequence to Sequence Models, Reinforcement Learning with Human Feedback (RLHF)

4.1 Relevance for Generative AI - Dimentionality Reduction, Data Denoising, Anomaly Detection, Image Generation, Feature Extraction, Latent Space Manipulation, Data Generation

4.2 Training of Autoencoders, Architecture - Encoder, Decoder and Latent Space (Bottleneck)

4.3 Types of Autoencoders - Vanilla Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Convolutional Autoencoders, Variational Autoencoders, Contractive Autoencoders, Stacked Autoencoders, Adversarial Autoencoders

4.4 Advance Architectures - Beta-VAE, Conditional Autoencoder, Seuqence to Sequence Autoencoder, and Graph Autoencoder  

5.1 Applications of GANs in Generative AI - Image Generation, Video Generation, Text to Image Synthesis, Music and Audio Generation, Style Transfer

5.2 Architecture - Generator, Discriminator, Adversarial Loss

5.3 Types of GANs - Vanilla GANs, Deep Convolutional GANs, Conditional GANs, Wasserstein GANs, Progressive Growing GANs, Cycle GANs, Style GANs, BigGANs, Pix2Pix.

5.4 Challenges - Mode Collapse, Non-Convergence, Vanishing Gradients

5.5 Advance Concepts (Attention GANs, 3D GANs, Speech GANs, Multi-Model GANs), Metrics (Inception Score, Fréchet Inception Distance, Perceptual Path Length)

5.6 Metrics - Inception Score, Fréchet Inception Distance, Perceptual Path Length

5.7 Key differences between Autoencoders and General Adversarial Networks

 Objective

1.1 Answering complex user queries using Retrieval-Augmented Generation (RAG).

1.2 Generating high-quality images using prompt.

1.4 Deploying the application for real-world use.

2.1 Text Query Answering Module (using RAG with Transformers).

2.2 Creative Writing Module (text generation using GPT or custom Transformer models).

2.3 Image Generation Module (using Diffusion Models like Stable Diffusion or DALL·E). 

2.4 Unified Frontend Interface for multi-modal interaction.

2.5 Backend API for serving models.

2.6 Deployment: Cloud-based or on-premise

3.1 Backend - FastAPI, Flask, Hugging Face Transformers, PyTorch, Tensorflow, OpenCV, Lancedb for Vector Search

3.2 Frontend - Gradio, Streamlit

3.3 Deployment - AWS, GCP, Azure, Docker, Kubernetes

4.1 A fully functional multi-modal AI assistant with text and image generation capabilities.

4.2 A deployed system accessible via a web interface.

4.3 A scalable architecture ready for real-world applications. 

5.1 Mastery of Retrieval-Augmented Generation (RAG) for text generation.

5.2 Hands-on experience with text-to-image generation.

5.3 Ability to fine-tune transformer models for creative writing and specific tasks.

5.4 Development of full-stack AI applications with backend and frontend integration.

5.5 Deployment of models using Docker and cloud platforms.

5.6 Knowledge of scalable AI systems with Kubernetes.

5.7 Practical experience in data preprocessing for text and image tasks.

5.8 Use of evaluation metrics for assessing generative models.

5.9 Documentation of systems and API integration for real-world applications.

5.10 Exposure to AI ethics, deployment best practices, and model security.

Our Certificates

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Program Fee

$1999.00 US Dollar


7352.00 Dirham


₹172500.00 + 18% GST


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