<< /S /GoTo /D (section.6.5) >> /D [347 0 R /XYZ 72 720 null] 214 0 obj << /S /GoTo /D (section.11.3) >> << /S /GoTo /D (section.5.1) >> 161 0 obj endobj 217 0 obj "Master's Thesis : Evaluation and Integration of Deep Learning Architectures for Automatic Defect Recognition", https://matheo.uliege.be/handle/2268.2/10716. 1. >> endobj (Sampling in an RBM) << /S /GoTo /D (chapter.13) >> << /S /GoTo /D (section.6.6) >> endobj A VGG model can have >500 MBs, whereas GoogleNet has a size of only 96 MB, Use of standard SGD instead of a fancy adaptive learning technique. Even on a decent GPU, it would take more than a week to get it to work. (Deep Belief Networks) 158 0 obj 42 0 obj A few more novel techniques which ResNet introduced are: The main advantage of ResNet is that hundreds, even thousands of these residual layers can be used to create a network and then trained. endobj The final architecture contains multiple of these inception modules stacked one over the other. Python code (in the form of Jupyter ipython notebooks) to support the book "Math and Architectures of Deep Learning".Code contributors: Ananya Ashok, Sujay Narumanchi, Devashish Shankar, Krishnendu Chaudhury. This is a bit different from usual sequential networks, where you see that there is reduced performance upgrades as you increase the number of layers. endobj << /S /GoTo /D (section.12.2) >> 117 0 obj Those who don’t know what ImageNet is, it is the dataset which is provided in ILSVR (ImageNet Large Scale Visual Recognition) challenge. endobj (Task) Hardly a day goes by without a new innovation or a new application of deep learning coming by. << /S /GoTo /D (section.10.6) >> (Running the Code) endobj endobj << /S /GoTo /D (chapter*.41) >> Next Article. (Tips and Tricks) endobj Very informative for deep learners.. http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/, this link might help people who like tensorflow…, can i named this assembled as pre training method for deep learning, Hi Sam, pretraining is a somewhat different concept – which essentially means that you dont have to train the model again to use them for prediction. (The Model) (Evaluation) 257 0 obj 121 0 obj 274 0 obj (Download) endobj endobj endobj (Prediction Using a Trained Model) (Implementing HMC Using Theano) 174 0 obj 126 0 obj endobj endobj If you have any questions on deep learning architectures, please feel free to share them with me through comments. 98 0 obj endobj >> endobj 150 0 obj Good Work Faizan. 241 0 obj << /S /GoTo /D (section.12.4) >> endobj (Putting it All Together) It can now theoretically be infinite (hence called extreme inception! endobj endobj ResNeXt is said to be the current state-of-the-art technique for object recognition. (Stacked Autoencoders) << /S /GoTo /D (chapter.7) >> endobj endobj Now similar to GoogleNet, these residual modules are stacked one over the other to form a complete end-to-end network. << /S /GoTo /D (section.6.3) >> 37 0 obj endobj << /S /GoTo /D (section.8.3) >> 53 0 obj << /S /GoTo /D (section.4.6) >> endobj endobj One follows the integration protocol and release an inference algorithm based on a pre-trained model, previously selected. The Open Access, that each student should known ! endobj << /S /GoTo /D [347 0 R /Fit ] >> For more information, see our Privacy Statement. This feeds the demand for innovative and efficient methods to inspect and discriminate between defective and non-defective parts. endobj I am doing research for deep learning in object detection. However, most of these advancements are hidden inside a large amount of research papers that are published on mediums like ArXiv / Springer. endobj endobj endobj 54 0 obj << /S /GoTo /D (section.5.4) >> endobj ��N��Y����@L:ںd���J�e}�&��$��+�y. endobj 62 0 obj (Running the Code) 253 0 obj 145 0 obj endobj Tags : AlexNet, Computer Vision, deep learning, deep learning architectures, inception, ResNet, VGG, Visual Recognition. endobj 149 0 obj << /S /GoTo /D (section.12.3) >> In a single layer, multiple types of “feature extractors” are present. << /S /GoTo /D (section.9.3) >> << /S /GoTo /D (section.14.4) >> endobj /D [357 0 R /XYZ 72 744.907 null] (Implementation) endobj The things which set apart this model is the scale at which it performs the task and the use of GPU for training. One such change is termed as an Xception Network, in which the limit of divergence of inception module (4 in GoogleNet as we saw in the image above) are increased. It is becoming very hard to stay up to date with recent advancements happening in deep learning. 41 0 obj 345 0 obj 10 Advanced Deep Learning Architectures Data Scientists Should Know! 330 0 obj endobj x�3PHW0Pp�2�A c(� endobj << /S /GoTo /D (section.9.4) >> 109 0 obj Interesting. << /S /GoTo /D (chapter.9) >> endobj endobj << /S /GoTo /D (section.10.2) >> << /S /GoTo /D (section.7.2) >> endobj 25 0 obj << /S /GoTo /D (section.7.3) >> (Putting it All Together) 29 0 obj endobj I’d love to be a part of this! I will aim to cover them in the subsequent article. /Font << /F37 352 0 R /F38 353 0 R /F40 354 0 R >> << /S /GoTo /D (section.4.5) >> (Hybrid Monte-Carlo Sampling) In the same time, an operator is responsible for manually inspect and detect defective part from non-defective ones, which can less and less be achieved in reasonable times for large amount of pieces. %PDF-1.4 endobj 226 0 obj 113 0 obj /D [347 0 R /XYZ 72 744.907 null] endobj 337 0 obj introduction to deep learning architectures for few-shot learning with pointers to core references. Can we be a part of that reading community?

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