Neural networks are driving the artificial intelligence growth in all the industries. The new inventions form Watson to Chatbots use neural networks of some form and shape. In order to be the data scientist of the future all of us have to learn and master the art of building and implementing neural networks. Here is a path to learn neural networks and master them –
- Start with fundamentals – Fundamentals of neural networks
This book explains the fundamentals of neural networks and how they are built. It is easy to read book with very less technical jargons. You can understand the book with minimum technical knowledge and you do not have to be technical or machine learning expert. A must read for aspiring data scientists.
2. Build your neural networks – Make your own neural network
From first book, you get to know fundamentals of building neural networks. It teaches you basics. The 2nd book teaches you details of how you can build your neural networks. It goes into details of neuron programming and activation functions of each neurons. Apart from theory, this is a great book to learn practical knowledge of building neural networks for projects in student life or work life.
3. Third book – Building blocks of AI
After reading first 2 books on basics of neural networks and how to build them, 3rd book is where you will go on a journey of Artificial Intelligence in a truly magical way. It teaches you how neural networks are building blocks of AI applications. This is where all the theory you have learnt on neural networks can be leveraged and applied to Artificial intelligence.
4. Fourth Book –
The fourth book is a practical guide of neural networks on pattern recognition. If you are interviewing or working in fraud detection area, this book is a definite read for you. Every fraud analyst should read this book to understand how to understand fraud patterns in the data using neural networks and apply them successfully in the job.
5. Time to learn the mathematics – Maths of neural networks
This book explains the mathematics of the neural networks in simple words. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.
6. Matlab never gets old – Neural networks in Matlab
Matlab never gets old and never goes out of fashion. Many of the data scientists still work on Matlab. Here is a book which teaches you everything you need to know to become master of neural networks using Matlab.
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