|
via Udemy |
Go to Course: https://www.udemy.com/course/learn-artificial-neural-network-from-scratch-in-python/
Certainly! Here is an engaging and comprehensive review and recommendation for the Coursera course on Artificial Neural Networks (ANN) from scratch: --- **Course Review and Recommendation: Artificial Neural Network (ANN) From Scratch on Coursera** If you're eager to delve into the world of Deep Learning and Artificial Neural Networks using Python, this course is an excellent starting point. Designed for learners who want a practical and hands-on approach, it guides you from fundamental concepts to building your own neural network models. **What Makes This Course Stand Out?** - **Comprehensive Content**: The course covers everything from Python basics to advanced neural network concepts. Whether you're a beginner or someone with some programming experience, the step-by-step approach makes it easy to follow and understand. - **Hands-On Learning**: Not just theory, the course emphasizes implementing neural networks from scratch using Python and Numpy, solidifying your understanding of the underlying mechanics such as forward propagation, backpropagation, gradient descent, and hyperparameter tuning. - **Real-World Application**: You will learn how to create models for classification and regression problems, which are crucial for solving various business and data science challenges. - **Structured Modules**: The course is well-organized into clear parts—from Python setup and basics to complicated concepts like backpropagation—making it flexible for learners to progress at their own pace. **What Will You Learn?** - Set up your Python environment and master essential libraries like Numpy, Pandas, Seaborn, and Matplotlib. - Gain a solid understanding of neural network architecture, neurons, and how they stack to form models. - Comprehend and implement key algorithms such as Gradient Descent and Backpropagation. - Develop and optimize neural network models in Python, including hyperparameter tuning. - Understand core deep learning concepts through practical numerical examples and model creation. **Who Should Take This Course?** This course is ideal for aspiring data scientists, machine learning enthusiasts, or professionals looking to expand their knowledge in deep learning. If you're starting your journey into neural networks or want to reinforce your understanding of how neural models work behind the scenes, this course provides all the necessary tools and techniques. **Final Verdict:** Highly recommended for anyone interested in building a strong foundation in neural networks and deep learning. The fact that all materials are free makes it accessible and an excellent starting point before moving on to more advanced courses. By the end of this course, you'll confidently build, test, and optimize neural network models tailored to specific business needs. **Conclusion:** Whether you're aiming for a career in AI or just want to understand how neural networks work, this course provides a practical, comprehensive, and beginner-friendly pathway to mastering ANN from scratch. Jump in and start building your neural network models today! --- Would you like me to help you craft a shorter summary or specific sections of this review?
Welcome to the course where we will learn about Artificial Neural Network (ANN) From Scratch!If you're looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python?You've found the right Neural Network course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python and ability to optimize the model tuning hyper parametersConfidently practice, discuss and understand Deep Learning conceptsThis course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.What is covered in this course?This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Python basics This part gets you started with Python and learn the brush up the basics like data structures, comprehensions, Object Oriented Programming and so on.This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas, Seaborn and matplotlib libraries.Part 2 - Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the neurons and how neurons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.Part 3 - Creating Regression and Classification ANN model in Python and RIn this part you will learn how to create ANN models in Python.We will learn how to model the neural network in two ways: first we model it from scratch and after that using scikit-learn library.Part 4 - Tutorial numerical examples on BackpropagationOne of the most important concept of ANN is backpropagation, so in order to apply the theory we learnt in lecture session in the real world neural networks, we are going to execute backpropagation taking one numerical example. We are going to take the help of partial differentiation and update the weights in backpropagation using gradient descent algorithms. By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.