For individuals who want a taste of machine learning, this book is a good choice (especially Deep Learning). The foundations of machine learning are covered at the beginning of this book, followed by chapters on convolutional neural networks, deep learning, and neural networks. The examples and case studies in this book use MATLAB as the underlying programming language and tool, blending basics and applications. This book is for those who want to use MATLAB to learn deep learning. Experience with MATLAB could be helpful. Using MATLAB for deep learning, learning about neural networks and multi-layer neural networks, working with convolution and pooling layers, and creating an MNIST example are all topics covered in this book. With the help of this book, you’ll be able to take on some of the difficult data issues, smart bots, and big data challenges of today.

Overview of MATLAB Deep Learning:

Deep learning is a technology that is gaining traction outside of traditional academic fields and is enabling applications like as self-driving cars, time series forecasting in the financial markets, and predictive fault monitoring of jet engines. Without needing to be an expert in the techniques, one can create a machine learning or deep learning model in MATLAB with fewer lines of code. Through model training and deployment, MATLAB offers the optimal platform for deep learning.
If you are a newbie, using MATLAB as a framework is highly beneficial. It offers a fantastic environment for learning mathematics through programming, including statistics and calculus. MATLAB proves to be highly beneficial when it comes to machine learning. MATLAB is helpful in fields like bioinformatics, computer vision, image processing, signal processing, and model tuning. It’s the ideal tool for data analysis and visualization.
This is the platform for you if you’re interested in using and learning machine learning mathematics. Although we might only utilize it for mathematical tasks, the framework offers packages that enable MATLAB provide environments for model creation. The Simulink is one of these programs.

Simulink offers a simulation- and model-based environment. It is a tool for graphical programming that was designed for embedded and dynamic systems. You can also design flowcharts for your models in MATLAB. Simulink permits it. Using flowcharts, you may create your model and all of its important phases. MATLAB includes a huge variety of these for a better knowledge in terms of code, significant features, and specific libraries.

Topics covered by this book:

  • Chapter 1 is about Machine Learning, what is machine learning, challenges of machine as well as the types of machine learning.

  • Next chapter 2 is about Neural Networks in which we study about layer and nodes of neural network as well as we will study about SGD, batch and mini batch.

  • After this chapter 3 is about Training of multi layer neural networks in which we study about back propagation Algorithms as well as cost functions and learning rule.

  • Chapter 4 is about neural networks and classification which is about binary and multiclass classification.

  • Chapter 5 is about deep leading in which we study about improvements of deep neural networks.

  • Last chapter is about convolutional neural networks which is about architecture of  convolutional Network as well as convolutional layer and pooling layer.

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