Udemy - Deep Learning Engineering From Data to Inference

Build a solid foundation in Deep Learning with Python, PyTorch, Julia, and MATLAB - from basics to advanced architecture
What you'll learn
Build and train deep neural networks from scratch using Python, PyTorch, Julia, and MATLAB.
Understand key mathematical concepts for deep learning, including vectors, derivatives, and probability.
Implement and optimize advanced architectures such as CNNs, RNNs, Transformers, and GNNs.
Apply deep learning techniques to real-world tasks like image classification, time series forecasting, and few-shot learning.
Requirements
Basic knowledge of Python programming is recommended but not mandatory.
A willingness to learn mathematical concepts such as vectors, derivatives, and probability.
Access to a computer with internet connection to install Python, Jupyter, and necessary libraries.
No prior deep learning experience required - everything will be taught from scratch.
Description
Master Deep Learning: From Fundamentals to Advanced ArchitecturesThis comprehensive course is designed to guide you through the entire deep learning landscape - starting from the foundations and moving toward cutting-edge techniques. You will begin by building strong fundamentals in Python programming, data preprocessing, and mathematical concepts critical to deep learning, including vectors, derivatives, and probability theory.You will learn how to train and optimize neural networks, explore classical architectures like Deep Feedforward Neural Networks (DFFNs), Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and dive into advanced models such as Residual Networks (ResNets), Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and Transformers.Practical coding sessions will teach you how to implement these architectures in Python using PyTorch, and additional examples with Julia (Flux) and MATLAB will broaden your perspective. You'll also cover frontier topics like Graph Neural Networks (GNNs), Bayesian Neural Networks, Federated Learning, Meta Learning, and HyperNetworks.Throughout the course, you will engage with hands-on exercises, real-world projects, and practical demonstrations. By the end, you will be capable of building, training, and evaluating deep learning models, as well as understanding their theoretical underpinnings.Whether you are a beginner looking to step into the world of artificial intelligence or a practitioner aiming to strengthen your skills, this course offers a structured and complete learning experience.Join us to unlock your deep learning potential!
Who this course is for
Aspiring machine learning engineers and data scientists who want to master deep learning.
Python developers aiming to transition into artificial intelligence and deep learning roles.
University students and researchers who need a structured understanding of deep learning architectures.
Anyone passionate about building and training modern neural networks from basic to advanced levels.
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