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Args: seed : Integer A non-negative integer that defines the random state. NumPy and random modules must be imported. import random import torch def set_seed ( seed = None, seed_torch = True ): """ Function that controls randomness. # Read more here: # Call `set_seed` function in the exercises to ensure reproducibility.
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# Set random seed # Executing `set_seed(seed=seed)` you are setting the seed # For DL its critical to set the random seed so that students can have a # baseline to compare their results to expected results. Section 7.4: Training ResNet from scratchīonus: Speed-Accuracy Trade-Off / Different Backbonesīonus Coding Exercise: Compare accuracy and training speed of different models Section 7.3: Train only classification layer
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Section 7.1: Download and prepare the data Think! 6.1: How do parameter savings depend the on number of input feature maps, 4 vs. Section 6.1: Depthwise separable convolutionsĬoding Exercise 6.1: Calculation of parameters Section 6: Depthwise separable convolutions Video 5: Improving efficiency: Inceptrion and ResNeXt Interactive Demo 3.2: What does AlexNet see?Ĭoding Exercise 4.1: Use the ResNet model Captain Silverhook's nautical figurehead (the shape carved on the front of his ship) has been destroyed, and he must. The story is about a pirate with a problem to solve. Kids in second grade or higher may be able to read the story independently. Video 1: Modern CNNs and Transfer LearningĬoding Exercise 1: Calculate number of parameters in FCNN vs ConvNet Captain Silverhook's Symbol of Strength is a 600-word short story suitable for children of all ages. Section 1: Modern CNNs and Transfer Learning Moving beyond Labels: Finetuning CNNs on BOLD responseįocus on what matters: inferring low-dimensional dynamics from neural recordings Vision with Lost Glasses: Modelling how the brain deals with noisy input Performance Analysis of DQN Algorithm on the Lunar Lander task NMA Robolympics: Controlling robots using reinforcement learning Something Screwy - image recognition, detection, and classification of screwsĭata Augmentation in image classification models Music classification and generation with spectrograms Knowledge Extraction from a Convolutional Neural Network Tutorial 1: Game Set-Up and Random Playerīonus Tutorial: Planning with Monte Carloĭeep Learning: Reinforcement Learning Wrap-upĮxample Model Project: the Train Illusion
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Tutorial 4: Model-Based Reinforcement Learning Tutorial 2: Learning to Act: Multi-Armed Bandits Tutorial 1: Un/Self-supervised learning methods Unsupervised And Self Supervised Learning (W3D3) Tutorial 1: Deep Learning Thinking 2: Architectures and Multimodal DL thinking
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Tutorial 1: Learn how to work with Transformers
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Tutorial 1: Introduction to processing time series Time Series And Natural Language Processing (W2D5) Tutorial 3: Conditional GANs and Implications of GAN Technologyīonus Tutorial: Deploying Neural Networks on the Web Tutorial 1: Variational Autoencoders (VAEs) Tutorial 1: Learn how to use modern convnetsīonus Tutorial: Facial recognition using modern convnets Tutorial 2: Deep Learning Thinking 1: Cost Functions Tutorial 2: Regularization techniques part 2ĭeep Learning: The Basics and Fine Tuning Wrap-up Tutorial 1: Regularization techniques part 1 Tutorial 1: Gradient Descent and AutoGrad Prerequisites and preparatory materials for NMA Deep Learning
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