1 00:00:00,050 --> 00:00:03,230 Lesson model, training techniques and best practices. 2 00:00:03,230 --> 00:00:09,110 Model training is a critical phase in the AI development lifecycle, particularly within the realm of 3 00:00:09,110 --> 00:00:10,520 development and testing. 4 00:00:11,300 --> 00:00:16,850 Effective model training involves a series of techniques and best practices that ensure the resulting 5 00:00:16,850 --> 00:00:22,640 models are accurate, reliable, and capable of generalizing well to unseen data. 6 00:00:23,420 --> 00:00:28,910 This lesson will delve into these techniques and best practices, providing a detailed examination of 7 00:00:28,910 --> 00:00:33,110 the processes and considerations essential for successful model training. 8 00:00:34,370 --> 00:00:40,280 The foundation of model training begins with data preparation, which is arguably the most time consuming 9 00:00:40,280 --> 00:00:42,200 and crucial part of the process. 10 00:00:42,620 --> 00:00:49,160 High quality data is imperative for training robust models, as the adage garbage in, garbage out suggests 11 00:00:49,760 --> 00:00:55,010 data must be cleaned, normalized, and often augmented to create a rich data set that reflects the 12 00:00:55,010 --> 00:00:58,130 diversity and complexity of real world scenarios. 13 00:00:58,490 --> 00:01:04,520 For instance, in image recognition tasks, data augmentation techniques such as rotation, scaling, 14 00:01:04,520 --> 00:01:10,470 and flipping are used to artificially expand the training data set, thereby improving the model's ability 15 00:01:10,470 --> 00:01:11,610 to generalize. 16 00:01:12,060 --> 00:01:17,700 Similarly, in natural language processing, tokenization, stemming, and lemmatization are pre-processing 17 00:01:17,730 --> 00:01:22,830 techniques that standardize text data, making it more suitable for model consumption. 18 00:01:24,690 --> 00:01:29,580 Once the data is prepared, the selection of an appropriate model architecture is the next critical 19 00:01:29,580 --> 00:01:30,210 step. 20 00:01:30,810 --> 00:01:33,870 Different tasks necessitate different types of models. 21 00:01:34,380 --> 00:01:40,260 For instance, convolutional neural networks are highly effective for image related tasks due to their 22 00:01:40,260 --> 00:01:42,960 ability to capture spatial hierarchies. 23 00:01:43,890 --> 00:01:49,830 Recurrent neural networks and their variants, such as long short term memory networks, are advantageous 24 00:01:49,830 --> 00:01:54,420 for sequential data tasks like time series prediction and language modeling. 25 00:01:55,380 --> 00:02:00,570 The choice of model architecture significantly impacts the performance and computational efficiency 26 00:02:00,570 --> 00:02:02,040 of the training process. 27 00:02:03,090 --> 00:02:07,140 Hyperparameter tuning is another essential aspect of model training. 28 00:02:07,680 --> 00:02:13,710 Hyperparameters are the configuration settings used to structure and train the model, such as learning 29 00:02:13,710 --> 00:02:17,970 rate, batch size, and the number of layers in a neural network. 30 00:02:18,390 --> 00:02:23,730 They are not learned from the data, but are set before the training begins and can profoundly influence 31 00:02:23,730 --> 00:02:25,110 the model's performance. 32 00:02:25,590 --> 00:02:31,530 Techniques such as grid search, random search, and Bayesian optimization are commonly employed to 33 00:02:31,530 --> 00:02:33,930 find the optimal set of hyperparameters. 34 00:02:34,470 --> 00:02:40,020 For example, a suitable learning rate ensures that the model converges efficiently without overshooting 35 00:02:40,020 --> 00:02:41,400 the optimal solution. 36 00:02:43,170 --> 00:02:48,960 Regularization techniques are pivotal for preventing overfitting, a scenario where the model performs 37 00:02:48,960 --> 00:02:52,350 well on training data but poorly on unseen data. 38 00:02:52,740 --> 00:02:57,750 Overfitting occurs when the model learns noise and details from the training data, to an extent that 39 00:02:57,750 --> 00:03:00,810 it negatively impacts its performance on new data. 40 00:03:01,290 --> 00:03:08,130 Techniques such as L1 and L2 regularization, dropout, and early stopping are employed to mitigate 41 00:03:08,130 --> 00:03:09,030 overfitting. 42 00:03:09,360 --> 00:03:15,090 Dropout, for instance, randomly drops units from the neural network during training, which helps 43 00:03:15,090 --> 00:03:20,910 in making the network less sensitive to the specific weights of neurons, thus generalizing better. 44 00:03:22,410 --> 00:03:27,210 The importance of a validation set cannot be overstated in the model training process. 45 00:03:27,900 --> 00:03:34,170 The validation set is used to tune the model's hyperparameters and assess its performance during training. 46 00:03:34,530 --> 00:03:38,970 It acts as a proxy for the test set and helps in detecting overfitting. 47 00:03:39,450 --> 00:03:45,660 A common practice is to split the available data into training, validation, and test sets, typically 48 00:03:45,660 --> 00:03:51,780 in a 70 to 15, 15, or 80 to 1010 ratio, depending on the size of the data set. 49 00:03:51,810 --> 00:03:57,060 This ensures that the model's performance metrics are a true reflection of its capability to generalize 50 00:03:57,060 --> 00:03:58,980 to new, unseen data. 51 00:04:00,480 --> 00:04:04,860 Model evaluation metrics are crucial for assessing the performance of a trained model. 52 00:04:05,190 --> 00:04:11,940 Different tasks require different metrics for classification tasks, metrics such as accuracy, precision, 53 00:04:11,940 --> 00:04:19,110 recall, F1 score, and the area under the receiver operating characteristic curve are widely used for 54 00:04:19,110 --> 00:04:20,220 regression tasks. 55 00:04:20,220 --> 00:04:25,350 Metrics such as mean squared error, mean absolute error, and r squared are standard. 56 00:04:25,590 --> 00:04:31,470 These metrics provide insights into various aspects of model performance, from overall accuracy to 57 00:04:31,500 --> 00:04:34,290 the balance between precision and recall. 58 00:04:34,950 --> 00:04:39,270 Cross-validation is a robust technique for model evaluation and selection. 59 00:04:39,510 --> 00:04:45,960 It involves partitioning the data into multiple subsets, training the model on some subsets, and validating 60 00:04:45,960 --> 00:04:47,460 it on the remaining ones. 61 00:04:47,910 --> 00:04:53,280 This process is repeated several times, and the results are averaged to produce a more reliable estimate 62 00:04:53,310 --> 00:04:54,780 of model performance. 63 00:04:55,320 --> 00:05:01,200 K fold cross-validation, where the data is divided into k subsets, is a commonly used method. 64 00:05:01,530 --> 00:05:07,350 This technique helps in making better use of limited data and provides a more comprehensive evaluation 65 00:05:07,350 --> 00:05:08,940 of the model's performance. 66 00:05:10,830 --> 00:05:16,830 The computational resources required for model training can be substantial, particularly for deep learning 67 00:05:16,830 --> 00:05:19,170 models with millions of parameters. 68 00:05:19,980 --> 00:05:26,310 Efficient utilization of hardware such as graphics processing units and tensor processing units can 69 00:05:26,310 --> 00:05:29,040 significantly accelerate the training process. 70 00:05:30,000 --> 00:05:35,520 Parallel and distributed training techniques, where the training workload is distributed across multiple 71 00:05:35,520 --> 00:05:41,100 machines or processors, are also employed to handle large scale data and complex models. 72 00:05:41,460 --> 00:05:48,270 Cloud based platforms such as Google Cloud AI, AWS SageMaker, and Microsoft Azure ML offer scalable 73 00:05:48,270 --> 00:05:53,970 solutions for training and deploying models providing the necessary computational power and infrastructure. 74 00:05:55,230 --> 00:06:01,320 Model interpretability and explainability are increasingly important, especially in high stakes domains 75 00:06:01,320 --> 00:06:03,180 such as healthcare and finance. 76 00:06:03,690 --> 00:06:09,330 Techniques such as shap and lime provide insights into how models make decisions, by attributing the 77 00:06:09,330 --> 00:06:12,450 contribution of each feature to the final prediction. 78 00:06:13,200 --> 00:06:20,250 This transparency is crucial for building trust in AI systems and for compliance with regulatory requirements 79 00:06:20,250 --> 00:06:24,180 such as the General Data Protection Regulation in the European Union. 80 00:06:25,620 --> 00:06:30,520 Finally, continuous monitoring and maintenance of trained models are essential to ensure their long 81 00:06:30,550 --> 00:06:31,870 term effectiveness. 82 00:06:32,680 --> 00:06:38,350 Models deployed in dynamic environments can experience performance degradation over time due to changes 83 00:06:38,350 --> 00:06:42,100 in data distribution, a phenomenon known as model drift. 84 00:06:42,520 --> 00:06:47,800 Regular retraining with updated data, along with monitoring tools that track model performance in real 85 00:06:47,830 --> 00:06:52,930 time, helps in maintaining the accuracy and reliability of AI systems. 86 00:06:55,060 --> 00:07:00,940 In conclusion, model training is a multifaceted process that involves meticulous data preparation, 87 00:07:00,940 --> 00:07:06,970 careful selection of model architecture, hyperparameter tuning, regularization, validation, and 88 00:07:06,970 --> 00:07:07,960 evaluation. 89 00:07:08,530 --> 00:07:14,710 Leveraging computational resources efficiently and ensuring model interpretability and continuous monitoring 90 00:07:14,710 --> 00:07:17,860 are also critical for building robust AI systems. 91 00:07:18,430 --> 00:07:23,530 By adhering to these techniques and best practices, AI practitioners can develop models that are not 92 00:07:23,530 --> 00:07:30,160 only accurate and reliable, but also capable of adapting to the complexities of real world applications.