PN
Priya Nair
last week
Beautifully produced and genuinely deep. The reader experience makes it easy to keep going for hours.
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Vizuara AI Labs · intermediate
Convolutional Neural Networks Fundamentals
Build CNNs from filters to a real classifier.
From a pixel grid to a trained network: convolution, pooling, activations, and backprop built by hand and illustrated. Ends with a working brain-tumor MRI classifier you code and train in Python.
34 capsules127 figures~7 hoursby Dr. Raj Dandekar
Read on your Kindle or e-reader
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00Getting Started4 capsules
Getting Started — 4 chapters.
01About This Bookconceptfree11 min02What You Will Learnconceptfree11 min03Where CNNs Fit in Deep Learningconcept🔒11 min04Setting Up Python and Your Toolscode🔒11 min01From Machine Learning to Deep Learning6 capsules
From Machine Learning to Deep Learning — 6 chapters.
05AI, ML, and Deep Learningconcept🔒12 min06Types of Machine Learning Modelsconcept🔒11 min07A Classical Baseline: Decision Treesintuition🔒12 min08The Six Steps of an ML Projectconcept🔒13 min09How ML Is Taught, and How We Will Teach Itintuition🔒12 min10Technical Overview of the Road Aheadconcept🔒12 min02Why Convolutional Neural Networks3 capsules
Why Convolutional Neural Networks — 3 chapters.
11Images as Numbers: Pixels, Channels, and Tensorsintuition🔒10 min12Why Dense Networks Fail on Imagesintuition🔒12 min13Introduction to Convolutional Neural Networksconcept🔒12 min03Filtering and Convolution5 capsules
Filtering and Convolution — 5 chapters.
14Filters in 1-D and the Convolution Operationmath🔒11 min15Convolution By Hand: A Worked Examplemath🔒13 min16Filters in 2-Dmath🔒14 min17Padding, Stride, and Output Sizemath🔒12 min18Feature Maps and Learned Filtersintuition🔒13 min04The Layers of a CNN5 capsules
The Layers of a CNN — 5 chapters.
19The Convolutional Layerconcept🔒12 min20Activation Functions and ReLUmath🔒13 min21Max Pooling Layersconcept🔒12 min22Flatten and the Fully Connected Headconcept🔒12 min23Stacking the Layers Into a CNNintuition🔒12 min05Training a CNN5 capsules
Training a CNN — 5 chapters.
24The Forward Pass and the Lossmath🔒11 min25Gradient Descent, Refreshedmath🔒13 min26Backpropagation in CNNsmath🔒11 min27CNN Architecture Explainedconcept🔒12 min28Landmark Architectures: LeNet to ResNetdeep-dive🔒13 min06Project: Brain Tumor Classifier5 capsules
Project: Brain Tumor Classifier — 5 chapters.
29The Dataset and the Problemproject🔒11 min30Building the CNN in Pythoncode🔒13 min31Training the Modelcode🔒13 min32Evaluating and Interpreting Resultsproject🔒10 min33Turning It Into an Applicationproject🔒13 min07Putting It All Together1 capsules
Putting It All Together — 1 chapter.
34Putting It All Togetherconcept🔒13 minRatings & reviews
1,034 readersRate this book
IR
Ishita Rao
last month
Rare mix of depth and readability. The worked examples are the clearest I have seen on this subject.
NK
Noah Kim
5 months ago
I read the free preview on a whim and ended up finishing the whole thing in two sittings. Superb pacing.
GS
Grace Sullivan
7 months ago
Dense but never confusing. Every chapter builds cleanly on the last — you can feel the care in the structure.