Deep learning is a subfield of machine learning that is concerned with the development of algorithms that are capable of learning from large amounts of data in an automated manner. Unlike traditional machine learning algorithms, deep learning algorithms are designed to learn from the data by building multiple levels of abstraction, where each level captures more complex and higher-level representations of the data.
The use of deep learning algorithms has revolutionized the field of artificial intelligence and has led to breakthroughs in many areas, including computer vision, natural language processing, speech recognition, and more.
In this chapter, we will introduce the concept of deep learning and explain how it differs from traditional machine learning algorithms. We will explore the key components of deep learning, including artificial neural networks and deep learning frameworks, and discuss how they are used to build and train deep learning models. We will also examine some of the most popular and effective deep learning algorithms and their applications.
Where to Learn More#
I’ve covered deep learning in-depth in a series of courses:
The series covers a wide variety of topics, including ANNs, CNNs, RNNs, LSTMs, GRUs, unsupervised deep learning, Autoencoders, Boltzmann Machines, word2vec, GloVe, recursive neural networks, deep reinforcement learning, computer vision, object detection, object localization, GANs, recommender systems, natural language processing, seq2seq, attention, transformers, and more.