Creating Custom Loss Functions using TensorFlow 2

Creating custom Loss functions using TensorFlow 2. Learning to write custom loss using wrapper functions and OOP in python

A neural network learns to map a set of inputs to a set of outputs from training data. It does so by using some form of optimization algorithm such as gradient descent, stochastic gradient descent, AdaGrad, AdaDelta or some recent algorithms such as Adam, Nadam or RMSProp. The ‘gradient’ in gradient descent refers to error gradient. After each iteration the network compares its predicted output to the real outputs, and then calculates the ‘error’. Typically, with neural networks, we seek to minimize the error. As such, the objective function used to minimize the error is often referred to as a cost function or a loss function and the value calculated by the ‘loss function’ is referred to as simply ‘loss’. Typical loss functions used in various problems – a. Mean Squared Error b. Mean Squared Logarithmic Error c. Binary Crossentropy d. Categorical Crossentropy e. Sparse Categorical Crossentropy In Tensorflow, these loss functions are already included, and we can just call them as shown below.

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This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.

What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.

Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.