Tips For Deep learning Aspirants

Tips for Aspirants and Works like a summary for Advanced Programmers in the field of AI

• You’ll usually need to preprocess raw data before feeding it into a neural network.
• When your data has features with different ranges, scale each feature independently as part of preprocessing.
• As training progresses, neural networks eventually begin to overfit and obtain worse results on never-before-seen data.
• If you don’t have much training data, use a small network with only one or two hidden layers, to avoid severe overfitting.
• If your data is divided into many categories, you may cause information bottlenecks if you make the intermediate layers too small.
• Regression uses different loss functions and different evaluation metrics than classification.
• When you’re working with little data, K-fold validation can help reliably evaluate your model
• Multiclass classification—A classification task where each input sample should be categorized into more than two categories: for instance, classifying handwritten digits.
• Multilabel classification—A classification task where each input sample can be assigned multiple labels. For instance, a given image may contain both a cat and a dog and should be annotated both with the “cat” label and the “dog” label. The number of labels per image is usually variable.
• Scalar regression—A task where the target is a continuous scalar value. Predicting house prices is a good example: the different target prices form a continuous space.
• Vector regression—A task where the target is a set of continuous values: for example, a continuous vector. If you’re doing regression against multiple values (such as the coordinates of a bounding box in an image), then you’re doing vector regression.
• Mini-batch or batch—A small set of samples (typically between 8 and 128) that are processed simultaneously by the model. The number of samples is often a power of 2, to facilitate memory allocation on GPU. When training, a mini-batch is used to compute a single gradient-descent update applied to the weights of the model.