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We’ll start with TensorFlow. TensorFlow works well on images as well as sequence-based data. If you are a beginner in deep learning, or don’t have a solid understanding of mathematical concepts like linear algebra and calculus, then the steep learning curve of TensorFlow might be daunting. I totally understand that this aspect can be complex for folks who are just starting out. My suggestion would be to keep practicing, keep exploring the community, and keep reading articles to get the hang of TensorFlow. Once you have a good understanding of the framework, implementing deep learning models will be very easy for you.
Keras is a pretty solid framework to start your deep learning journey. If you are familiar with Python and are not doing some high-level research or developing some special kind of neural network, Keras is for you. The focus is more on achieving results rather than getting bogged down by the model intricacies. So if you are given a project related to, say image classification or sequence models, start with Keras. You will be able to get a working model very quickly. Keras is also integrated in TensorFlow and hence you can also build your model using tf.keras.
As compared to TensorFlow, PyTorch is more intuitive. One quick project with both these frameworks will make that abundantly clear. Even if you don’t have a solid mathematics or a pure machine learning background, you will be able to understand PyTorch models. You can define or manipulate the graph as the model proceeds which makes PyTorch more intuitive. PyTorch does not have any visualization tool like TensorBoard but you can always use a library like matplotlib. I wouldn’t say PyTorch is better than TensorFlow, but both these deep learning frameworks are incredibly useful.
Scikit-learn is a Python library used for machine learning. More specifically, it’s a set of simple and efficient tools for data mining and data analysis. The framework is built on top of several popular Python packages, namely NumPy, SciPy, and matplotlib. It’s easy to use even for beginners – and a great choice for simpler data analysis tasks. On the other hand, scikit-learn is not the best choice for deep learning.
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.