Published 09/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 91 lectures (6h 13m) | Size: 2 GB
Deep Learning & Deep Neural Networks made super easy for absolute beginners without digging deep into harsh mathematics
What you’ll learn
• The basics of Machine Learning.
• The basics of Neural Networks.
• The implementation of Logistic Regression.
• The architecture of Neural Networks.
• The basics of training a Deep Neural Network (DNN) using Gradient Descent Algorithm.
• The creation of a complete structure for DNN from scratch using Python.
• The implementation of DNN on any dataset.
• Using Deep Learning for IRIS dataset.
• The implementation of a complete DNN using Numpy.
• And much more…
Requirements
• No prior knowledge of Deep Learning or Math is needed. You will start from the basics and build your knowledge of the subject step by step.
• A willingness to learn and practice.
• Only basic knowledge of Python.
Description
Comprehensive Course Description
Want to master the essential Deep Learning concepts fast? Ready to train your machine like how a father would teach his son?
Then this is the right course!
Yes, we know you can choose from lots of similar courses and lectures out there regarding DNNs. But this truly step-by-step course is different!
In every new video tutorial, you will build on what you have already learned and advance one extra step. You strengthen your learning by solving the small task that we assign at the end of each video before you proceed to the next one.
You start by first learning the theoretical part of a concept. Next, you implement everything in this concept practically using Python.
Right at the beginning of this comprehensive course, you will train your machine using the power of Python. Your machine will learn just like humans. Then, it will start making predictions based on that learning!
Python is the programming language used in this course. In the first section, you will learn Python from a basic level up to a more advanced level so that you can implement any deep learning concept.
You’ll also learn about the different steps involved in data preprocessing, which allows you to prepare the data for deep learning algorithms.
You’ll learn all the general concepts of machine learning (ML), as well. You’ll then implement one of the key ML algorithms, “Deep Neural Networks.”
The applications of Deep Learning are varied. From self-driving cars to visual recognition, natural language processing to virtual assistance, fraud detection to the colorization of black and white images, and healthcare to language translations, Deep Learning is indispensable across industries.
In the foreseeable future, the demand for Deep Learning experts is expected to remain high. Start a rewarding career with this course and get your foot in this recession-proof field. The upside is you get a firsthand opportunity to solve some of the world’s most challenging problems!
This in-depth course is right for you if you have some programming knowledge or even if you know nothing about Deep Learning and DNNs!
This course is comparable to other Deep Learning courses that cost thousands of dollars. The difference is you can learn all that information by paying a fraction of that price! With 6+ hours of HD video lectures that are subdivided into 85 short videos and code notebooks for every address, you’ll not find a more exhaustive course on Deep Neural Networks and Deep Learning on Udemy!
Why Should You Enroll in This Course?
You will quickly learn how to build DNNs and how to train them in this course. This primarily learning by doing course will also help you master the elementary concepts and methodology with regards to Python.
This course is
Clear and easy to understand.
Self-explanatory and flows smoothly.
Practical with live coding.
Concise and to the point.
Thorough, covering the most sophisticated and recently discovered DNN models by eminent data scientists.
Teaching Is Our Passion
Our focus is on creating online learning content that encourages learning by doing. As a result, you get more than a superficial look at DNNs. For instance, the mini-projects in the concluding sections will help you to see for yourself the practical, real-world implementation of DNNs.
We put in the extra effort so that it’s easier for you to understand every concept clearly. In this easy-paced course, you will gain a sound understanding of the fundamental concepts before you move onward to the more complex ones. The main course materials include high-quality short videos, course notes, handouts, and evaluation exercises. Another advantage is that in case you have any course-related doubts, you can get in touch with our friendly team.
Course Content
You’ll learn programming with Python and how to use it for data preprocessing and DNNs! A few topics that you will be learning include
1. Programming with Python.
2. NumPy with Python for array handling.
3. Using Pandas Data Frames to handle Excel Files.
4. Data Preprocessing.
5. Machine Learning concepts, including basics of ML, Gradient Descent, Logistic Regression, Feed Forward, Back Propagation, Model Optimization, etc.
6. DNNs from absolute scratch using NumPy.
7. Implementing DNNs on different datasets.
8. How can we use DNN for IRIS classification, and much more!
Enroll in this course and become a Deep Learning Expert today!
Completing this course successfully will enable you to
· Relate the theoretical concepts and sequence modeling with DNNs.
· Understand the methodology of DNNs with Deep Learning using real-world datasets.
Who is this course for
Anyone interested in Data Science.
People interested in taking their data speak to a higher level.
People who want to master DNNs with real datasets in Deep Learning.
Individuals passionate about numbers and programming.
People who want to implement DNNs in realistic projects.
Data Scientists.
Business Analysts.
Who this course is for
• Anyone interested in Data Science.
• People interested in taking their data speak to a higher level.
• People who want to master DNNs with real datasets in Deep Learning.
• Individuals passionate about numbers and programming.
• People who want to implement DNNs in realistic projects.
• Data Scientists.
• Business Analysts.
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