This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course will help you understand the importance of deep learning techniques. You will examine simple models like perceptron before learning more complex yet powerful deep learning models. The course will provide hands-on practical knowledge of how to code simple and complex deep learning models in NumPy, a powerful Python library and Keras, a cutting-edge library for deep learning in Python. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.
This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will ...Show More
TAKEAWAY SKILLS
Content
1.
✨Before We Begin
2 Lessons
Get familiar with the basics of deep learning and practical Python coding skills.
2.
✨Introduction to Deep Learning
4 Lessons
Grasp the fundamentals of machine learning paradigms, deep learning principles, and neural networks.
3.
✨Simple Perceptron Models in NumPY
16 Lessons
Work your way through perceptron models, coding, prediction methods, and optimization techniques using NumPy.
4.
✨Towards Deep Neural Networks in NumPY
8 Lessons
Break down the steps to foundational concepts of neural networks and their training processes.
5.
🖥️ Project: Build a Letter Classification Model
8 Lessons
Solve problems in building and training a letter classification model with deep learning.
6.
✨Building Deep Learning Models with Keras
11 Lessons
Tackle building, compiling, training, evaluating, and utilizing deep learning models using Keras.
7.
✨Fine-tune Keras Model
8 Lessons
Master the steps to optimize, validate, and hypertune deep learning models for improved accuracy.
8.
🖥️ Project: Build a Digit Recognition Model
4 Lessons
Try out building and evaluating a digit recognition model using the MNIST dataset.
9.
✨Conclusion
2 Lessons
Walk through the key points of ANNs, RNNs, and CNNs and their applications.
Certificate of Completion
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