Neural Networks in Python from Scratch: Complete guide
Neural Networks in Python from Scratch: Complete guide, available at $79.99, has an average rating of 4.54, with 78 lectures, 2 quizzes, based on 541 reviews, and has 4537 subscribers.
You will learn about Learn step by step all the mathematical calculations involving artificial neural networks Implement neural networks in Python and Numpy from scratch Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others Build neural networks applied to classification and regression tasks Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch This course is ideal for individuals who are Beginners who are starting to learn about Artificial Neural Networks or Deep Learning or People interested in the theory of Artificial Neural Networks or Undergraduate students who are studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence or Artificial Neural Networks It is particularly useful for Beginners who are starting to learn about Artificial Neural Networks or Deep Learning or People interested in the theory of Artificial Neural Networks or Undergraduate students who are studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence or Artificial Neural Networks.
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Summary
Title: Neural Networks in Python from Scratch: Complete guide
Price: $79.99
Average Rating: 4.54
Number of Lectures: 78
Number of Quizzes: 2
Number of Published Lectures: 74
Number of Published Quizzes: 2
Number of Curriculum Items: 80
Number of Published Curriculum Objects: 76
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn step by step all the mathematical calculations involving artificial neural networks
- Implement neural networks in Python and Numpy from scratch
- Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others
- Build neural networks applied to classification and regression tasks
- Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch
Who Should Attend
- Beginners who are starting to learn about Artificial Neural Networks or Deep Learning
- People interested in the theory of Artificial Neural Networks
- Undergraduate students who are studying subjects related to Artificial Intelligence
- Anyone interested in Artificial Intelligence or Artificial Neural Networks
Target Audiences
- Beginners who are starting to learn about Artificial Neural Networks or Deep Learning
- People interested in the theory of Artificial Neural Networks
- Undergraduate students who are studying subjects related to Artificial Intelligence
- Anyone interested in Artificial Intelligence or Artificial Neural Networks
Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.
One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. This is due to the fact that the majority of the materials that are available are very technical and apply a lot of mathematical formulas, which simply makes the learning process incredibly difficult for whomever wishes to take their first steps in this field. With this in mind, the main objective of this course is to present the theoretical and mathematical concepts of neural networks in a simple yet thorough way, so even if you know nothing about neural networks, you’ll understand all the processes. We’ll cover concepts such as perceptrons, activation functions, multilayer networks, gradient descent and backpropagation algorithms, which form the foundations through which you will understand fully how a neural network is made. We’ll also cover the implementations on a step-by-step basis using Python, which is one of the most popular programming languages in the field of Data Science. It’s important to highlight that the step-by-step implementations will be done without using Machine Learning-specific Python libraries, because the idea behind this course is for you to understand how to do all the calculations necessary in order to build a neural network from scratch.
To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts.
Are you ready to take the next step in your professional career? I’ll see you in the course!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction and course content
Lecture 2: Get the materials
Chapter 2: Single layer perceptron
Lecture 1: Plan of attack
Lecture 2: Applications of artificial neural networks
Lecture 3: Biological fundamentals
Lecture 4: Artificial neuron
Lecture 5: Perceptron
Lecture 6: Perceptron implementation 1
Lecture 7: Perceptron implementation 2
Lecture 8: Weight update 1
Lecture 9: Weight update 2
Lecture 10: Perceptron implementation 3
Lecture 11: Perceptron implementation 4
Lecture 12: Perceptron implementation 5
Lecture 13: Additional reading
Lecture 14: Homework instruction
Lecture 15: Homework solution
Chapter 3: Multilayer perceptron
Lecture 1: Plan of attack
Lecture 2: Introduction to multilayer neural networks
Lecture 3: Activation functions
Lecture 4: Sigmoid function implementation
Lecture 5: Hidden layer activation 1
Lecture 6: Hidden layer activation 2
Lecture 7: Multilayer perceptron implementation 1
Lecture 8: Multilayer perceptron implementation 2
Lecture 9: Output layer activation
Lecture 10: Multilayer perceptron implementation 3
Lecture 11: Error calculation (loss function)
Lecture 12: Multilayer perceptron implementation 4
Lecture 13: Basic algorithm
Lecture 14: Gradient descent and derivative
Lecture 15: Multilayer perceptron implementation 5
Lecture 16: Output layer delta
Lecture 17: Multilayer perceptron implementation 6
Lecture 18: Hidden layer delta
Lecture 19: Multilayer perceptron implementation 7
Lecture 20: Backpropagation and learning rate
Lecture 21: Weight update with backprogation 1
Lecture 22: Multilayer perceptron implementation 8
Lecture 23: Weight update with backprogation 2
Lecture 24: Multilayer perceptron implementation 9
Lecture 25: Multilayer perceptron implementation 10
Lecture 26: Iris dataset
Lecture 27: Bias, error and multiple outputs
Lecture 28: Hidden layers
Lecture 29: Output layer with categorical data
Lecture 30: Stochastic gradient descent
Lecture 31: Deep learning
Lecture 32: Additional reading
Lecture 33: Homework instruction
Lecture 34: Homework solution
Chapter 4: Libraries for neural networks
Lecture 1: Plan of attack
Lecture 2: Pybrain 1
Lecture 3: Pybrain 2
Lecture 4: Homework instruction: iris dataset
Lecture 5: Homework solution
Lecture 6: Sklearn for classification 1
Lecture 7: Sklearn for classification 2
Lecture 8: Sklearn for classification 3
Lecture 9: Sklearn for regression
Lecture 10: Homework instruction: wine classification
Lecture 11: Homework solution
Lecture 12: TensorFlow for image classification 1
Lecture 13: TensorFlow for imagem classification 2
Lecture 14: TensorFlow for image classification 3
Lecture 15: Homework instruction: fashion mnist classification
Lecture 16: Homework solution
Lecture 17: PyTorch for classification 1
Lecture 18: PyTorch for classification 2
Lecture 19: PyTorch for classification 3
Lecture 20: Homework instruction: diabetes classification
Lecture 21: Homework solution
Lecture 22: Final remarks
Chapter 5: Congratulations!! Don't forget your Prize 🙂
Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
-
Jones Granatyr
Professor -
SuperDataScience Team
Helping Data Scientists Succeed -
Ligency Team
Helping Data Scientists Succeed -
AI Expert Academy
Instructor
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 8 votes
- 3 stars: 55 votes
- 4 stars: 186 votes
- 5 stars: 288 votes
Frequently Asked Questions
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