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MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.

https://github.com/janishar/mit-deep-learning-book-pdfTags | learning pdf machine-learning good mit deep-learning neural-network book machine linear-algebra neural-networks deeplearning print excercises lecture-notes chapter clear thinking printable |

Implementation | Java |

License | Public |

Platform | OS-Independent |

This content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts. I'd like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In my opinion, it is one of the bedrock of machine learning, deep learning and data science.

Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorch"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksDeep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearningThis repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

deep-learning-tutorial machine-learning machinelearning deeplearning neural-network neural-networks deep-neural-networks awesome-list awesome list deep-learningBy Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

awesome awesome-list unicorns list lists resources deeplearning deep-learning deep-neural-networks neural-network neural-networks music music-information-retrieval audio audio-processing article music-genre-classification bib machine-learning researchA generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflowThe Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegThis is another (work in progress) Chinese translation of Michael Nielsen's Neural Networks and Deep Learning, originally my learning notes of this free online book. It's written in LaTeX for better look and cross-referencing of math equations and plots. And I borrowed some finished work from https://github.com/tigerneil/neural-networks-and-deep-learning-zh-cn. To compile the source code to a PDF file, please make sure you have a latest TeX system installed. You can download and install a TeX distribution for your platform from http://tug.org.

NOTE: Building on the momentum of deeplearn.js, we have joined the TensorFlow family and we are starting a new ecosystem of libraries and tools for Machine Learning in Javascript, called TensorFlow.js. This repo moved from PAIR-code/deeplearnjs to tensorflow/tfjs-core. A part of the TensorFlow.js ecosystem, this repo hosts @tensorflow/tfjs-core, the TensorFlow.js Core API, which provides low-level, hardware-accelerated linear algebra operations and an eager API for automatic differentiation.

deep-learning typescript webgl machine-learning neural-network deep-neural-networks gpu-accelerationThis repository is first and foremost a comprehensive machine learning framework for the game of Go, focussing on deep learning techniques. What you'll find here is a library that builds up from the game-play basics to very advanced techniques. In particular, you find code for early approaches in game AI, intermediate techniques using deep learning, to implementations of AlphaGo and AlphaGo Zero - all presented in one common framework. You can install this library with pip and follow the examples in the code folder. On the other hand, this repository at the same time contains Code, and sample chapters for the book "Deep Learning and the Game of Go" (Manning), available for early access here, which ties into the library and teaches its components bit by biy. If you're following the code samples from the book, check out the branches for individual chapters.

deep-learning neural-networks machine-learning data-science games game-of-go alphago alphago-zeroDeep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

tensorflow word-embeddings gru autoencoder gans doc2vec skip-thoughts adagrad cyclegan deep-learning-mathematics capsule-network few-shot-learning quick-thought deep-learning-scratch nadam deep-learning-math lstm-math cnn-math rnn-derivation contractive-autonencodersSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnPyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others. Neural networks are a subclass of computation graphs. Computation graphs receive input data, and data is routed to and possibly transformed by nodes which perform processing on the data. In deep learning, the neurons (nodes) in neural networks typically transform data with parameters and differentiable functions, such that the parameters can be optimised to minimise a loss via gradient descent. More broadly, the functions can be stochastic, and the structure of the graph can be dynamic. So while neural networks may be a good fit for dataflow programming, PyTorch's API has instead centred around imperative programming, which is a more common way for thinking about programs. This makes it easier to read code and reason about complex programs, without necessarily sacrificing much performance; PyTorch is actually pretty fast, with plenty of optimisations that you can safely forget about as an end user (but you can dig in if you really want to).

deep-learningGorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

machine-learning artificial-intelligence neural-network computation-graph differentiation gradient-descent gorgonia deep-learning deeplearning deep-neural-networks automatic-differentiation symbolic-differentiation go-libraryGorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.

machine-learning deep-neural-networks deep-learning neural-network automatic-differentiation artificial-intelligence computation-graph deeplearning gradient-descent hacktoberfest differentiation symbolic-differentiation graph-computationKur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.

deep-learning deep-neural-networks speech-recognition deep-learning-tutorial machine-learning neural-networks neural-network image-recognition speech-to-textConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.

artificial-intelligence neural-networks machine-learning deep-learningCompared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

machine-learning deep-learning lstm human-activity-recognition neural-network rnn recurrent-neural-networks tensorflow activity-recognitionTrending deep learning Github repositories can be found here. Hint: This will be updated regularly.

deep-learning deep-neural-networks deep-reinforcement-learning convolutional-neural-networks recurrent-neural-networks stargazers-count artificial-neural-networks artificial-intelligence machine-learning top-repositories
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