New release, 0.12.0 is available, with additional chapter on using DL for regression and predicting the prices of Boston real estate (a classic regression example).
On Friday, October 25, 2019 at 9:53:26 AM UTC+2, Dragan Djuric wrote: > > New release:Deep Learning for Programmers: An Interactive Tutorial with > CUDA, OpenCL, MKL-DNN, Java, and Clojure > > https://aiprobook.com/deep-learning-for-programmers > <https://aiprobook.com/deep-learning-for-programmers?release=0.11.0&source=cgroups> > > + Chapter on Adaptive Learning Rates > > ** > no middleman! > 100% of the revenue goes towards my open-source work! > > ** this is the only DL book for programmers > > - interactive & dynamic > - step-by-step implementation > - incredible performance, yet no C++ hell (!) > - Intel & AMD CPUs (MKL-DNN) > - Nvidia GPUs (CUDA and cuDNN) > - AMD GPUs (yes, OpenCL too!) > - Clojure (it’s magic!) > - Java Virtual Machine (without Java boilerplate!) > - complete source code > - beautiful typesetting (see the sample chapters) > > Current status: > > ## Table of Contents > > ### Part 1: Getting Started > > 4-6 chapters, (TO BE DETERMINED) > > ### Part 2: Inference ([AVAILABLE]) > > #### Representing layers and connections ([AVAILABLE]) > > #### Bias and activation function ([AVAILABLE]) > > #### Fully connected inference layers ([AVAILABLE]) > > #### Increasing performance with batch processing ([AVAILABLE]) > > #### Sharing memory ([AVAILABLE]) > > #### GPU computing with CUDA and OpenCL ([AVAILABLE] > > ### Part 3: Learning ([AVAILABLE] > > #### Gradient descent and backpropagation ([AVAILABLE] > > #### The forward pass ([AVAILABLE] > > #### The activation and its derivative ([AVAILABLE] > > #### The backward pass ([AVAILABLE] > > ### Part 4: A simple neural networks API ([AVAILABLE] > > #### Inference API ([AVAILABLE] > > #### Training API ([AVAILABLE] > > #### Initializing weights ([AVAILABLE] > > #### Regression: learning a known function ([AVAILABLE] > > ### Part 5: Training optimizations (IN PROGSESS) > > #### Weight decay ([AVAILABLE] > > #### Momentum and Nesterov momentum ([AVAILABLE] > > #### Adaptive learning rates ([AVAILABLE] > > #### Regression: Boston housing prices (SOON) > > #### Dropout (SOON) > > #### Stochastic gradient descent (SOON) > > #### Classification: IMDB sentiments (SOON) > > ### Part 6: Tensors (TO BE DETERMINED, BUT SOON ENOUGH) > > #### Tensors, Matrices, and ND-arrays (TBD) > > #### Tensors on the CPU with MKL-DNN (TBD) > > #### Tensors on the GPU with cuDNN (TBD) > > #### Tensor API (TBD) > > ### Part 7: Convolutional layers (TBD) > > 4-6 Chapters, (TBD) > > ### Part 8: Recurrent networks (TBD) > > 4-6 Chapters, (TBD) > > -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/clojure/7ee9973b-bfb2-4bda-bf00-8ad4adbdb9f1%40googlegroups.com.
