Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will:
Deep Neural Network for Beginners Using Python
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the basics of training a DNN using the Gradient Descent algorithm.
Apply knowledge to implement a complete DNN using NumPy.
Analyze and create a complete structure for DNN from scratch using Python.
Evaluate and work on a project using deep learning for the IRIS dataset.
Skills you'll gain
Details to know
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Recently updated!
September 2024
Assessments
3 assignments
Taught in English
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There are 5 modules in this course
In this module, we will provide a brief overview of the course and introduce the instructor. We will also outline the learning objectives and what students can expect to achieve by the end of the course.
What's included
3 videos1 reading
In this module, we will delve into the foundational aspects of deep learning. We will start by examining a real-world problem and progressively introduce key concepts such as perceptrons, linear equations, and error functions. This section includes hands-on coding exercises to solidify understanding.
What's included
37 videos
In this module, we will focus on more advanced topics in deep learning. We will cover gradient descent, logistic regression, and the architecture of neural networks. Practical coding sessions will help learners apply these concepts and build their own deep learning models.
What's included
31 videos1 assignment
In this module, we will address optimization challenges in deep learning. Topics include underfitting vs. overfitting, regularization techniques, and strategies to overcome common issues like local minima and vanishing gradients. Learners will gain insights into improving their model's performance and reliability.
What's included
10 videos
In this module, we will undertake a comprehensive final project, applying all the concepts and skills learned throughout the course. Starting with data exploration and progressing through model training and testing, this project will solidify your understanding and ability to implement deep learning solutions.
What's included
5 videos2 assignments
Instructor
Offered by
Recommended if you're interested in Machine Learning
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.