Deep Learning Online Course
Deep Learning Online Course
Elevate your machine learning expertise with our comprehensive course on deep neural networks and unlock the potential of deep learning. This practical course will equip you with a strong foundation in deep learning fundamentals, covering topics such as artificial neural networks, activation functions, bias, data, and loss functions. You will gain a foundational understanding of Python with a focus on data science, along with essential skills for data cleaning and analysis, creating visualizations using Matplotlib, and working effectively with NumPy and Pandas.
Building on this foundation, you will delve into the realm of deep learning, beginning with the MP Neuron model and advancing through the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem.
Through practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of today's most widely used deep learning frameworks. You will learn to build and train deep neural networks, assess their performance, and fine-tune them for optimal results. By the end of the course, you will be well-equipped to advance toward becoming a deep learning expert.
Who is this course for?
This course is perfect for anyone eager to explore deep learning and build a strong foundation in artificial neural networks. It requires no prior programming or machine learning experience, making it an excellent starting point for beginners. Whether you're a student, a professional, or someone looking to enhance your skills and stay current with the latest advancements in artificial intelligence, this course is designed for you.
Course Curriculum
- Introduction
- Getting the Basics Right
- Python Crash Course on Basics
- Python for Data Science - Crash Course
- MP Neuron Model
- MP Neuron in Python
- Summary of MP Neuron
- Perceptron
- Perceptron in Python
- Sigmoid Neuron
- Sigmoid Neuron Implement with Python
- Basic Probability
- Deep Neural Networks
- Universal Approximation Theorem
- Deep Learning with TensorFlow 2.x
- Activation Functions in Deep Learning Neural Networks
- Applying Deep Learning
