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Coursera - Device-based Models with TensorFlow Lite - AD-TEAM - 12-03-2024 386.03 MB | 00:05:09 | mp4 | 1280X720 | 16:9 Genre:eLearning |Language:English
Files Included :
01 introduction-a-conversation-with-andrew-ng (37.84 MB) 01 a-few-words-from-laurence (4.83 MB) 02 features-and-components-of-mobile-ai (2.74 MB) 03 architecture-and-performance (5.86 MB) 06 optimization-techniques (3.87 MB) 01 saving-converting-and-optimizing-a-model (6.49 MB) 02 examples (3.09 MB) 03 quantization (5.1 MB) 04 tf-select (4.93 MB) 06 paths-in-optimization (3.71 MB) 01 running-the-models (4.71 MB) 02 transfer-learning (10.15 MB) 03 converting-a-model-to-tflite (4.72 MB) 04 transfer-learning-with-tflite (15.55 MB) 01 introduction-a-conversation-with-andrew (3 MB) 02 installation-and-resources (4.17 MB) 04 architecture-of-a-model (1.3 MB) 05 initializing-the-interpreter (3.49 MB) 06 preparing-the-input (2.21 MB) 07 inference-and-results (2.88 MB) 01 code-walkthrough (7.29 MB) 02 run-the-app (6.76 MB) 01 classifying-camera-images (2.65 MB) 02 initialize-and-prepare-input (6.22 MB) 01 demo-of-camera-image-classifier (13.35 MB) 01 initialize-model-and-prepare-inputs (5.01 MB) 02 inference-and-results (5.04 MB) 01 demo-of-the-object-detection-app (4.68 MB) 02 code-for-the-inference-and-results (7.13 MB) 01 introduction-a-conversation-with-andrew-ng (6.41 MB) 02 a-few-words-from-laurence (6.04 MB) 03 what-is-swift (3.72 MB) 04 tensorflowliteswift (1.96 MB) 06 cats-vs-dogs-app (2.56 MB) 07 taking-the-initial-steps (4.15 MB) 09 scaling-the-image (2.92 MB) 11 more-steps-in-the-process (4.87 MB) 01 looking-at-the-app-in-xcode (13.45 MB) 02 what-have-we-done-so-far-and-how-do-we-continue (3.72 MB) 03 using-the-app (2.73 MB) 04 app-architecture (3.36 MB) 05 model-details (1.4 MB) 07 initial-steps (5.91 MB) 09 final-steps (2.87 MB) 01 looking-at-the-code-for-the-image-classification-app (13.62 MB) 02 object-classification-intro (2.57 MB) 03 tfl-detect-app (6.42 MB) 04 app-architecture (1.16 MB) 06 initial-steps (1.27 MB) 07 final-steps (6.03 MB) 08 looking-at-the-code-for-the-object-detection-model (10.66 MB) 01 introduction-a-conversation-with-andrew-ng (17.13 MB) 02 a-few-words-from-laurence (6.09 MB) 03 devices (5.42 MB) 01 starting-to-work-on-a-raspberry-pi (3.23 MB) 02 how-do-we-start (4.52 MB) 04 image-classification (1.67 MB) 06 the-4-step-process (3.11 MB) 07 object-detection (4.16 MB) 09 back-to-the-4-step-process (7.09 MB) 01 raspberry-pi-demo (7.96 MB) 01 microcontrollers (7.77 MB) 04 closing-words-by-laurence (2.74 MB) 02 a-conversation-with-andrew-ng (8.37 MB)] Screenshot
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