TensorFlow is a popular open-source Python-based machine learning platform built by Google. It is widely used for training deep learning models and has many benefits over other similar platforms, such as Numpy. For example, TensorFlow automatically computes gradients, allowing developers to save time and effort. It also offers flexible deployment options, running on CPUs, GPUs, and TPUs, and can be easily distributed across multiple devices. Additionally, TensorFlow can be exported to other runtimes, making it a versatile platform for a wide range of machine learning tasks.
One of the key advantages of TensorFlow is that it is a platform rather than a library. This means that it provides a range of tools and services that make it easier to develop, train, and deploy machine learning models. TensorFlow also offers excellent scalability, allowing developers to easily train large and complex models on multiple devices.
Keras is an API built on top of TensorFlow that simplifies the process of training deep learning models. It is designed to prioritize the developer experience, reducing cognitive load and providing clear and actionable feedback. Keras offers a wide range of different workflows, ranging from high-level abstractions that make it easy to quickly build and train models, to low-level APIs that provide fine-grained control over every aspect of the training process.
To get started with deep learning using TensorFlow and Keras, you will need to set up a development environment. While it is possible to run deep learning experiments on a CPU, it is highly recommended to use a modern GPU for best performance. There are several options for accessing a GPU for deep learning, including buying a physical GPU, using GPU instances on the cloud, or using the free GPU runtime provided by TensorFlow.
Once you have set up your development environment, there are several tools that you can use to create and run deep learning experiments. Jupyter notebooks are a popular choice for this purpose, as they allow you to break up your experiments into smaller pieces and run them interactively. Additionally, Colaboratory offers a free Jupyter notebook environment with built-in support for TensorFlow, making it easy to get started quickly.
TensorFlow offers a range of tools and APIs for creating and training deep learning models. At a low level, TensorFlow provides tools for manipulating tensors and performing tensor operations. This is the underlying infrastructure that is used to build and train machine learning models. At a higher level, TensorFlow provides APIs for building and training deep learning models using layers, loss functions, optimizers, and metrics. This makes it easy to quickly create and train complex models using TensorFlow.
To get started with TensorFlow, you will need to understand two key concepts: tensors and variables. Tensors are the data structures that are used to represent the inputs and outputs of machine learning models. They can be thought of as multidimensional arrays of numbers. Variables, on the other hand, are used to represent the state of a model, and can be updated during training. To retrieve the gradient of a model with respect to its inputs and variables, you can use the GradientTape scope provided by TensorFlow.