Mastering Deep Learning for Generative AI Build Creative Applications with Machine Learning
Master the Machine Muse: Build Generative AI with ML
Development ,Data Science,Deep Learning
Lectures -41
Duration -9 hours
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Course Description
Unlock the creative potential of artificial intelligence with "Master the Machine Muse: Build Generative AI with ML." This comprehensive course takes you on an exciting journey into the world of generative AI, blending the art of machine learning with the science of creativity. Whether you're an aspiring data scientist, a tech enthusiast, or a creative professional looking to harness the power of AI, this course will provide you with the skills and knowledge to build and deploy your generative models.
Course Highlights:
- Introduction to Generative AI: Understand the fundamentals of generative AI and its applications across various domains such as art, music, text, and design.
- Foundations of Machine Learning: Learn the core concepts of machine learning, including supervised and unsupervised learning, and how they apply to generative models.
- Deep Learning for Creativity: Dive deep into neural networks and explore architectures like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformers that are driving the generative AI revolution.
- Hands-On Projects: Engage in practical, hands-on projects that will guide you through the process of building your generative models. From generating art to composing music, you'll experience the thrill of creating with AI.
- Python Programming: Gain proficiency in Python programming, focusing on libraries and frameworks essential for generative AI, such as TensorFlow, PyTorch, and Keras.
- Ethics and Future of Generative AI: Discuss the ethical considerations and future implications of generative AI, ensuring you are well-equipped to navigate this rapidly evolving field responsibly.
Who Should Enroll:
- Data Scientists and Machine Learning Engineers looking to specialize in generative models.
- Artists, Musicians, and Designers interested in exploring AI as a tool for creativity.
- Tech Enthusiasts and Innovators eager to stay ahead in the field of AI.
- Students and Professionals aiming to enhance their skill set with cutting-edge technology.
Goals
By the end of this course, you will:
- Have a strong grasp of generative AI concepts and techniques.
- Be able to build and train generative models using state-of-the-art machine learning frameworks.
- Understand the ethical considerations and potential impacts of generative AI.
- Be prepared to apply generative AI skills in real-world projects and innovative applications.
Prerequisites
- Basic understanding of Python programming.
- Familiarity with machine learning concepts is beneficial but not required.

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Deep Learning Concepts
3 Lectures
-
The History of Deep Learning and Inspired by Neuroscience 10:06 10:06
-
Understanding Neural Networks: Weights, Multi-Neuron Networks, 11:58 11:58
-
Dive Deep into Backpropagation 10:53 10:53
Recurrent Neural Networks (RNNs)
3 Lectures

Advanced Training Techniques
3 Lectures

Convolutional Neural Networks (CNNs)
3 Lectures

Advanced CNN Techniques
3 Lectures

Implementing CNNs
3 Lectures

Deep Learning for Computer Vision
3 Lectures

Advanced Techniques in Image Processing
3 Lectures

Model Interpretation and Optimization
3 Lectures

Deployment and Maintenance of Deep Learning Models
3 Lectures

Advanced Deployment Techniques
3 Lectures

Advanced Data Analysis
3 Lectures

Clustering Techniques
3 Lectures

Advanced Clustering and Evaluation
2 Lectures

Instructor Details

AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!
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