Deep Learning - Crash Course 2023

Description

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks. This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas. With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications. Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results. By the end of the course, you will be well on your way to becoming a deep learning expert in no time.

Learn about the fundamentals of Python and some of its well-known libraries

Understand the fundamentals of deep learning and neural networks

Build and train your own deep neural network models

Learn different activation functions and optimization algorithms

Learn techniques for improving model performance and reducing overfitting

Apply deep learning to real-world problems in various fields

This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.

The course adopts a practical, hands-on learning approach that focuses on building intuition and mastery through implementation. Students will work through a series of exercises and projects designed to help them understand the fundamental concepts of deep learning and develop their skills in Python programming, data analysis, and neural network design.

Practical hands-on exercises to build intuition on deep neural networks * Learn essential concepts in deep learning through practical applications * A comprehensive understanding of neural network architecture and design

https://github.com/PacktPublishing/Deep-Learning---Crash-Course-2023

Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence. With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.

1. Welcome

1. Welcome on Board

2. Getting the Basics Right

3. Python Crash Course on Basics

4. Python for Data Science - Crash Course

5. MP Neuron Model

5. MP Neuron Model

6. MP Neuron in Python

7. Summary of MP Neuron

7. Summary of MP Neuron

8. Perceptron

9. Perceptron in Python

10. Sigmoid Neuron

11. Sigmoid Neuron Implement with Python

12. Basic Probability

12. Basic Probability

13. Why Deep Neural Networks - Intuition

13. Deep Neural Networks

14. Universal Approximation Theorem

15. Deep Learning with TensorFlow 2.x

16. Activation Functions in Deep Learning Neural Networks

17. Applying Deep Learning