List of resources to learn deep learning
- Tiny Datasets for experimenting with Deeplearning Algorithms.
- List of the biggest machine learning datasets from across the web.
Learn about basics of Machine Learning:
- Machine learning - Video series
- What is Machine Learning - Comparison with X
- DanB machine learning tutorials
- High level view of ML
- One hot encoding
- Feature Engineering
- Lexical Analysis Terminologies
- Xgboost example 1
- Xgboost example 2
- Google Machine Learning Crash Course
- ML Cheatsheet with various Python examples
- History of deep learning - very high level overview
- How to Make Data Amazing
- Super awesome psudo code of all important Deep Learning algorithms
- Terminologies
- What is intuition in Deep Learning?
- Deep Learning tutorials by DanB
- Deep Learning Course by Kaggle
- Backpropogation in Python from scratch
- Saving Keras model and weights
- Getting started with the Keras Sequential model
- Gradient Descent Optimization Algorithms
- Model Loss Functions in Keras
- Linear Algebra for Deep Learning cheat sheet
- Linear Algebra for Deep Learning cheat sheet 2
- In-depth
- Friendly guide to CNN
- CNN Cat vs Dog in Keras
- Really good CNN notebook demo + Flask
- Easy to follow CNN digit recogniser tutorial
- Capsule Network
- CNN for beginners
- CNN in detail by Stanford
- CNN cifar10 implementation in TF
- CNN cifar10 implementation in TF 2
- Andrew Ng Youtube series
- CNN Striding
- Dropout
- Keras classification example
- Cat vs Dog classification tutorial
- Cat vs Dog classification Keras
- VGG paper
- Deep learning for image denoising and superresolution
- Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
- Predict Sentiment From Movie Reviews Using Deep Learning
- LSTM + Word Embedding IMDB
- A Word2Vec Keras tutorial
- Kears Embedding Visualisation
- Nice ML and DL training
- DanB Kaggle Kernels
- My First Month as a Junior Data-Scientist
- How to read Maths notations
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Curbing the roofline: a scalable and flexible architecture for CNNs on FPGA
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CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices
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Energy-Efficient CNN Implementation on a Deeply Pipelined FPGA Cluster
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
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Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs
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Maximizing CNN Accelerator Efficiency Through Resource Partitioning
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A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA
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Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
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Optimizing Loop Operation and Dataflow in FPGA Acceleration of Deep Convolutional Neural Networks
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Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
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Why TanH is a Hardware Friendly Activation Function for CNNs
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F-CNN: An FPGA-based framework for training Convolutional Neural Networks
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A New Implementation of Fully Programmable Complete CNN Processor Core on FPGA
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Implementing a CNN Universal Machine on FPGA: state-of-the-art and key challenges
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Caffeinated FPGAs: FPGA framework For Convolutional Neural Networks
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Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks
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fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs
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The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGA
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A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
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An efficient FPGA implementation of a DT-CNN for small image gray-scale pre-processing
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Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
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PipeCNN- An OpenCL-Based Open-Source FPGA Accelerator for Convolution Neural Networks