Deep learning mit pdf pdf#
Deep learning mit pdf software#

Greedy Layer-wise Unsupervised Pretraining(436KB).

Overview of Representation Learning(10.1MB) Video.

Learning manifolds with Autoencoders(21.8MB) Video_UB, Video_IISc.

Knowledge Representation, Reasoning & QA(466KB).

Neural Machine Translation(1.1MB) Video.

Sequence Modeling: Recurrent and Recursive Nets.

Convolution-Pooling as Strong Prior(416KB).

Optimization Strategies and Meta-Algorithms.

Approximate Second-Order Methods: Newton, BFGS(838KB).

Adaptive Learning Rates: RMSProp, Adam(1MB).

Parameter Initialization Strategies(84KB).

Basic Algorithms: SGD and Momentum(3.2MB).

Challenges in Neural Network Optimization(2.6MB).

How Learning Differs from Pure Optimization(628KB).

Optimization for Training Deep Learning(2.5MB) Video.

Tangent Distance, Tangent Propagation(11.8MB) Video.

Parameter Tying and Parameter Sharing(104KB).

Regularization and Underconstrained Problems(1.2MB).

Norm Penalties as Constrained Optimization(127KB).

Backward Propagation and Differentiation.

Challenges Motivativating Deep Learning(2.4MB).

Building a Machine Learning Algorithm(259KB).

Unsupervised Learning Algorithms(299KB).

Hyperparameters and Validation Sets(490KB).

Capacity:Underfitting,overfitting(1.7MB).

Numerical Computation and Gradient-based Optimization.

Probability and Information Theory(2.1MB).

Linear Algebra for Machine Learning(2.1MB).

Applied Math and Machine Learning Basics.

Historical Trends in Deep Learning(9.9MB).

Deep learning mit pdf pdf#

Lecture videos for Deep Learning Research (Part III) are also indexed at Youtube Channel on Deep Learning Research.ДЇollowing are links to pdf slide files for topics. Video recordings of on-line lectures on Chapter 12.3 (Natural Language Processing) onwards are available at topic links given below. Lectures on Chapters 1-11 given at the Indian Institute of Science in Spring 2020 can be found on youtube. Applied Math and Machine Learning basics, II. There are twenty chapters organized into three major subdivisions (after the Overview): I. Courville, Deep Learning, MIT Press, 2016.

A course on Probabilistic Graphical Models (PGMs) is helpful but not necessary.Д¬ourse material loosely follows the organization of the text: I.

Pre-requisite: Introductory Machine Learning (ML).

Deep learning mit pdf software#

Students will be encouraged to use open source software libraries such as Tensorflow. Methods to train and optimize the architectures and methods to perform effective inference with them, will be the main focus. The course will cover connectionist architectures commonly associated with deep learning, e.g., basic neural networks, convolutional neural networks and recurrent neural networks. The course, which will be taught through lectures and projects, will cover the underlying theory, the range of applications to which it has been applied, and learning from very large data sets. They have been successful in many artificial intelligence problems including image classification, speech recognition and natural language processing.

Such algorithms have been effective at uncovering underlying structure in data, e.g., features to discriminate between classes. Deep Learning algorithms learn multi-level representations of data, with each level explaining the data in a hierarchical manner.