This repository is your one stop guide for becoming a machine learning engineer. Over time, I'll add a complete step-by-step roadmap for you to follow to becoming a machine leearning engineer.
First, let's start with one of the hottest topics in ML - Deep Learning.
Understanding Deep Learning is much easier if you have a conceptual grasp over the following topics:
- AI Theory
- Machine Learning
- Linear Algebra
- Calculus
- Probability and Statistics, and of course,
- a programming language, preferrably Python
The list of resources below will get you started with Deep Learning.
-
Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. This book introduces a broad range of topics in deep learning. The authors include the pioneers of deep learning, Yoshua Bengio is one of the three godfathers of deep learning, Ian Goodfellow is popular for his creation of Generative adversarial Networks (GANs). The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. The book is available for free in HTML format here.
-
Deep Learning for coders with fastai and PyTorch by Jeremy Howard & Sylvain Gugger (O'Reilly) This is an excellent book for practical introduction to deep learning. It's backed by a free online resource that's a must have resource if you want to become a deep learning practitioner. Check it out at Fast.ai.
-
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems This is an excellent book for practitionars. It's recommended by TensorFlow on their website and serves as a first step resource for developers wanting to learn TensorFlow. Just check out it's ratings on Amazon.