What you would learn in Machine Learning: Neural networks from the scratch course?
The course will teach you how we'll build a neural system entirely from scratch, using no specific libraries. While we'll use the Python programming language, by the conclusion of this course, you'll be able to build a neural system in every programming language.
We will learn how neural networks function intuitively, then mathematically. We will also discover several essential techniques that help stabilize the training of neural networks (log-sum-exp trick) and stop the memory used in training from expanding rapidly (jacobian-vector product). Without these techniques, many neural networks are not able to be developed.
We will build our neural networks using real-world image classification and regression issues. To accomplish this, we will use various cost functions and also a variety of activation functions.
This course targets people looking to build a neural network from scratch and those looking to learn how a neural network functions starting from the A-Z.
This course is taught with Python, which is the Python programming language. It requires basic programming knowledge. If you don't have the necessary background, I suggest you brush up on your programming abilities by attending a crash course in programming. It is also suggested that you are familiar with Algebra and Analysis concepts to benefit from this course the most.
The concepts covered are :
The creation of neural networks right from scratch
Gradient descent and Jacobian matrix
The development of Modules that can be nested to build a complex neural structure
The log-sum-exp trick
Jacobian vector product
The activation function (ReLU, Softmax, LogSoftmax, ...)
Cost-related functions (MSELoss, NLLLoss, ...)
What are neural networks?
Create a neural network from the ground up (Python, Java, C, ...)
Training neural networks
The activation function, along with the universal approximation rule
Acquire more knowledge about Machine Learning and Data Science
Implementation techniques: Jacobian-Vector Product and the log-sum-exp trick
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