## https://github.com/chasingbob/deep-learning-resources## deep-learning-resourcesA Collection of resources I have found useful on my journey finding my way through the world of Deep Learning. ## CoursesStanford CS231n Convolutional Neural Networks for Visual Recognition Coursera: Neural Networks for Machine Learning Even though Deep Learning is a small but important subset of Machine Learning, it is still important to get a wider knowledge and understanding of Machine Learning and no course will give you a better understanding than the excellent course by Andrew Ng. ## TutorialsA Beginner's Guide To Understanding Convolutional Neural Networks An Intuitive Explanation of Convolutional Neural Networks Hacker's guide to Neural Networks ~Andrej Karpathy Gradient Descent Optimisation Algorithms Recurrent Neural Networks Keras is my favorite framework for Deep Learning and is underneath compatible with both Theano and Tensorflow. The Keras Blog - Building powerful image classification models using very little data How convolutional neural networks see the world ~Francois Chollet A complete guide to using Keras as part of a TensorFlow workflow
TensorFlow A Few Useful Things to Know about Machine Learning ~Pedro Domingos YouTube: Introduction to Deep Learning with Python YouTube: Machine Learning with Python YouTube: Deep Visualization Toolbox Yes you should understand backprop ~Andrej Karpathy PDF: Dropout: A Simple Way to Prevent Neural Networks from Overfitting PDF: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size Quora: How does a confusion matrix work PDF: Understanding the difficulty of training deep feedforward neural networks ## Books & e-BooksNeural Networks and Deep Learning Deep Learning Book - some call this book the Deep Learning bible ## Getting PhilosophicalWhat is the next likely breakthrough in Deep Learning Looking at The major advancements in Deep Learning in 2016 gives us a peek into the future of deep learing. A big portion of the effort went into Generative Models, let us see if that is the case in 2017. Do machines actually beat doctors? ## CompetitionsKaggle is the place to be for Data Scientists and Deep Learning experts at the moment - but you don't have to be an expert to feel the adrenalin of a $150000 competition Kaggle competitions perfect for deep learning: ## Tools of the Trade## Python## MatplotLibDeep Learning is far from being an exact science and a lot of what you do is based on getting a feel for the underlying mechanics, visualising the moving parts makes it easier to understand and Matplotlib is the go-to library for visualisation YouTube: Bare Minimum: Matplotlib for data visualization ## NumPyNumPy is a fast optimized package for scientific computing, and is also the underlying library a lot of Machine Learning frameworks are build on top of. Becoming a NumPy ninja is an important step to mastery. ## keras-visualsVisualise the training of your Keras model with an easy to use Matplotlib graph using one line of code. ## Datasets20 Weird & Wonderful Datasets for Machine Learning ## Whom I followAndrew Ng | Homepage | Twitter François Chollet | Homepage | Github Twitter Ian Goodfellow | Homepage | Github | Twitter Tshilidzi Mudau | Twitter Yann LeCun | Yann LeCun | Twitter | Quora Mike Tyka | Homepage | Twitter Jason Yosinski | Homepage | Twitter | Youtube Andrej Karpathy | Homepage | Twitter | G+ Chris Olah | Homepage | Github | Twitter Yoshua Bengio | Homepage |