Course Overview
Jump Start your Deep Learning journey in our two-day, hands-on Deep Learning Essentials Boot Camp, where you'll learn to employ deep learning, a potent subset of machine learning that utilizes artificial neural networks to mimic human cognition. This skill will equip you to analyze complex data, make informed predictions, and significantly contribute to your organization's success.
This comprehensive course covers a broad array of crucial topics. You'll explore the deep learning environment, become proficient with TensorFlow and Keras, and comprehend the principles of neural networks. You'll also learn to handle data preprocessing, model tuning, and optimization, as well as model deployment using TensorFlow Serving. As part of the interactive curriculum, 40% of the course is dedicated to hands-on lab work. This experience allows you to apply your newfound knowledge to real-world projects, such as implementing neural networks, enhancing model performance, and deploying trained models on a server.
By the conclusion of this course, you'll possess a robust foundation in deep learning with Python. You'll be competent in creating, training, and optimizing deep learning models, and applying these skills to solve data-centric problems within your organization. Under the guidance of an industry expert and with exposure to cutting-edge tools, this course promises to be a valuable stepping-stone in your deep learning journey.
Who should attend
This intermediate and beyond level course is geared for experienced professionals aiming to apply machine learning and deep learning to solve complex business problems, including product managers, data analysts, data scientists, developers, team leads, and other technical stakeholders who want to leverage deep learning for strategic decisions. It's also suited for those who are in roles that require them to work with data, understand patterns, or make predictions, such as business analysts, software developers, and researchers. Python experience is required.
Prerequisites
To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:
- Python programming is required, as the labs revolve around leveraging Python. Basic skills in handling and manipulating data using Python libraries such as NumPy and Pandas would be advantageous.
- Familiarity with concepts such as variables, functions, control flow, and data structures will ensure a smooth learning experience.
- While the course will introduce deep learning from scratch, having a grasp of basic machine learning concepts will be beneficial.
- Some understanding of algebra and basic calculus will be helpful in comprehending the mathematical components of deep learning.
Course Objectives
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:
- Gain a firm grasp of the fundamentals of deep learning, understanding the theory and math that powers it.
- Get comfortable with Anaconda and Jupyter Notebook, two essential tools in a data scientist's arsenal.
- Understand how to build, train, and deploy neural networks using Python, TensorFlow, and Keras.
- Dive deep into Python and its powerful libraries used for deep learning, becoming proficient in TensorFlow and Keras.
- Learn the art of data preprocessing, a critical skill in preparing data for machine learning models.
- Learn to tune and optimize your deep learning models to ensure they deliver the best performance possible. Uncover the mystery of various optimizers and learn to choose the right one.
- Bonus Content: Exploring GPT and its role in Deep Learning, and applying Generative AI to deep learning
Outline: Deep Learning Essentials Boot Camp (TTAI3012)
Introduction to Deep Learning
- Understand the concept and significance of deep learning in modern business.
- Fundamental concepts in deep learning like neurons, layers, weights, bias, and activation functions.
- Real-world use cases of deep learning in business.
Setting up Deep Learning Environment
- Understand how to create an effective deep learning environment.
- Basics of Anaconda and Jupyter notebook.
- Lab: Set up a Python environment
Introduction to TensorFlow and Keras
- Get an overview of TensorFlow and Keras.
- Learn the process of creating a simple neural network using Keras.
- Lab: Build a basic neural network
Fundamentals of Neural Networks
- Understand what neural networks are and how they function.
- Learn about forward propagation and backpropagation in a neural network.
- Lab: Implement a Multi-Layer Perceptron (MLP) on a simple dataset
Working with Data in Deep Learning
- Understand the importance of data preprocessing in deep learning.
- Learn how to handle and preprocess different types of data - images, text, etc.
- Lab: Preprocess a dataset for a deep learning task
Tuning and Optimizing Deep Learning Models
- Learn about different types of optimizers - SGD, Adam, RMSprop, etc.
- Learn how to save and load trained models.
- Lab: Tune and optimize a neural network model
Deploying Deep Learning Models
- Understand how to deploy deep learning models.
- Learn about serving models with TensorFlow Serving.
- Lab: Deploy a trained model
Real-world Applications of Deep Learning
- Understand the real-world applications of deep learning.
- Overview of deep learning in healthcare, finance, transportation, and more.
Bonus Chapters / Time Permitting
Bonus: Introduction to GPT and its Role in Deep Learning
- Understand what GPT (Generative Pretrained Transformer) is and how it contributes to Deep Learning.
- Explore how GPT models work and their architecture.
- Discover GPT applications in natural language understanding and generation tasks.
- Learn how GPT can assist in various job roles, such as customer service, content creation, etc.
- Understand the ethical considerations around using generative models.
- Lab
Bonus: Deep Dive into Generative AI for Deep Learning
- Understand the principles of Generative AI and its function in Deep Learning.
- Discover different types of generative models such as Generative Adversarial Networks (GANs) and their applications.
- Learn about the role of generative AI in image generation, text-to-image synthesis, and more.
- Understand how generative AI can improve productivity and creativity in job roles involving digital content and design.
- Discussion on the challenges of using generative AI and future trends.
- Lab