No need for manual feature selection; deep learning automatically learns features from raw data (like images or audio).

No need for manual feature selection; deep learning automatically learns features from raw data (like images or audio).

• 1.
  What is DeepLearning
• 2.
  What is DeepLearning? • Deep learning is a type of machine learning that teaches computers to perform tasks by learning from examples, much like humans do. • In technical terms, deep learning uses something called "neural networks," which are inspired by the human brain. These networks consist of layers of interconnected nodes that process information. The more layers, the "deeper" the network, allowing it to learn more complex features and perform more sophisticated tasks.
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  The Evolution ofMachine Learning to Deep Learning
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  The importance offeature engineering • Feature engineering is the process of selecting, transforming, or creating the most relevant variables, known as "features," from raw data to use in machine learning models. • For example, if you're building a weather prediction model, the raw data might include temperature, humidity, wind speed, and barometric pressure. Feature engineering would involve determining which of these variables are most important for predicting the weather and possibly transforming them (e.g., converting temperature from Fahrenheit to Celsius) to make them more useful for the model. • In traditional machine learning, feature engineering is often a manual and time-consuming process that requires domain expertise. However, one of the advantages of deep learning is that it can automatically learn relevant features from the raw data, reducing the need for manual intervention.
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  Why is DeepLearning Important? • The reasons why deep learning has become the industry standard: • Handling unstructured data: Models trained on structured data can easily learn from unstructured data, which reduces time and resources in standardizing data sets. • Handling large data: Due to the introduction of graphics processing units (GPUs), deep learning models can process large amounts of data with lightning speed. • High Accuracy: Deep learning models provide the most accurate results in computer visions, natural language processing (NLP), and audio processing. • Pattern Recognition: Most models require machine learning engineer intervention, but deep learning models can detect all kinds of patterns automatically.
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  How Deep LearningWorks • Deep learning uses feature extraction to recognize similar features of the same label and then uses decision boundaries to determine which features accurately represent each label. In the cats and dogs classification, the deep learning models will extract information such as the eyes, face, and body shape of animals and divide them into two classes. • The deep learning model consists of deep neural networks. The simple neural network consists of an input layer, a hidden layer, and an output layer. Deep learning models consist of multiple hidden layers, with additional layers that the model's accuracy has improved.
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  What is DeepLearning Used For? • Recently, the world of technology has seen a surge in artificial intelligence applications, and they all are powered by deep learning models. The applications range from recommending movies on Netflix to Amazon warehouse management systems. • In this section, we are going to learn about some of the most famous applications built using deep learning. This will help you realize the full potential of deep neural networks.
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  Computer Vision • Computervision (CV) is used in self-driving cars to detect objects and avoid collisions. It is also used for face recognition, pose estimation, image classification, and anomaly detection.
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  Automatic Speech Recognition •Automatic speech recognition (ASR) is used by billions of people worldwide. It is in our phones and is commonly activated by saying "Hey, Google" or "Hi, Siri." Such audio applications are also used for text-to-speech, audio classification, and voice activity detection.
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  Translation • Deep learningtranslation is not limited to language translation, as we are now able to translate photos to text by using OCR, or translate text to images by using NVIDIA GauGAN2 .
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  Biomedical • This fieldhas benefited the most with the introduction of deep learning. DL is used in biomedicine to detect cancer, build stable medicine, for anomaly detection in chest X-rays, and to assist medical equipment.
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  Deep Learning Models •Let's learn about different types of deep learning models and how they work. • Supervised Learning • Supervised learning uses a labeled dataset to train models to either classify data or predict values. The dataset contains features and target labels, which allow the algorithm to learn over time by minimizing the loss between predicted and actual labels. Supervised learning can be divided into classification and regression problems.
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  Classification • The classificationalgorithm divides the dataset into various categories based on feature extractions. The popular deep learning models are ResNet50 for image classification and BERT (language model)) for text classification.
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  Regression • Instead ofdividing the dataset into categories, the regression model learns the relationship between input and output variables to predict the outcome. Regression models are commonly used for predictive analysis, weather forecasting, and predicting stock market performance. LSTM and RNN are popular deep learning regression models.
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  Unsupervised Learning • Unsupervisedlearning algorithms learn the pattern within an unlabeled dataset and create clusters. Deep learning models can learn hidden patterns without human intervention and these models are often used in recommendation engines. • Unsupervised learning is used for grouping various species, medical imaging, and market research. The most common deep learning model for clustering is the deep embedded clustering algorithm.
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  Reinforcement Learning • Reinforcementlearning (RL) is a machine learning method where agents learn various behaviors from the environment. This agent takes random actions and gets rewards. The agent learns to achieve goals by trial and error in a complex environment without human intervention. • Just like a baby with encouragement from its parents learns to walk, the AI learns to perform certain tasks by maximizing rewards, and the designer sets the rewards policy. Recently, RL has seen high demands in automation due to advancements in robotics, self- driving cars, defeating pro players in games, and landing rockets back to earth.