Deep Learning is a form of machine learning that employs artificial neural networks with multiple layers of processing to extract increasingly complex features from data. Deep Learning, a subset of Machine Learning, utilizes these neural networks to learn from vast datasets without the need for explicit programming. These networks draw inspiration from the human brain and find applications in tasks such as image recognition, speech comprehension, and language processing. Various types of deep learning networks, including feedforward neural networks, convolutional neural networks, and recurrent neural networks, exist. While Deep Learning thrives with abundant labeled data and powerful computing resources, it demonstrates excellent results across numerous applications.
Why use Deep Learning?
While Deep Learning can tackle intricate challenges, Machine Learning, in general, can be applied to virtually any problem by converting it into numerical data and programming it to identify patterns. This versatility extends to inputs and outputs from diverse domains.
A guiding principle is to first consider building a simple rule-based system if it can effectively address the task without the need for machine learning. Deep Learning excels in addressing complex problems, such as:
- Problems with an extensive set of rules: When traditional approaches falter, machine learning or deep learning can provide solutions.
- Dynamic or evolving environments: Deep learning exhibits adaptability to new scenarios and changing conditions.
- Extracting insights from large datasets: Attempting to manually craft rules for comprehensively understanding and analyzing extensive data sets can be impractical.
However, there are instances where Deep Learning may not be the optimal choice:
- When interpretability is crucial: Deep learning models often produce patterns that are challenging for humans to interpret.
- When a traditional approach suffices: If a straightforward rule-based system can achieve the desired outcome, it may be preferable.
- When errors are unacceptable: Deep learning models don’t always yield predictable outputs, making them less suitable for applications where errors are intolerable.
- When data is limited: Deep learning models typically thrive with a substantial amount of data to yield robust results.
Also, in case you got unstructured data like PDF’s, Image’s, Audio files, and Video files we use Deep Learning. All the models we built in our previous blogs are in other words called Shallow Learning Algorithms. Shallow learning, also termed as shallow machine learning, involves employing relatively uncomplicated models with a minimal number of layers or processing stages. These models usually possess a restricted ability to comprehend intricate patterns from data. Shallow learning algorithms, such as linear regression, logistic regression, decision trees, k-nearest neighbors, and support vector machines, fall into this category. They are frequently applied in situations where the data exhibits straightforward patterns, and the connections between features and outcomes are uncomplicated.
Contrastingly, deep learning employs neural networks with numerous layers to autonomously acquire intricate patterns and representations from data. This approach has demonstrated remarkable efficacy across diverse domains, including image and speech recognition, natural language processing, and game playing. Models in deep learning, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Fully connected neural networks and transformer neural networks excel at capturing intricate relationships in data by iteratively extracting hierarchical features.
What are neural networks?
Neural networks aim to mimic the functionality of the human brain, merging principles from computer science and statistics to address prevalent challenges within the realm of artificial intelligence. Below is the flow of how we can leverage neural networks for deep learning.
Anatomy of a Neural Networks
Note: patterns in hidden layers will be called embedding, weights, feature representation, feature vectors in loose terms, but all of them means the same.
There are different types of learnings: –
- Supervised learning
- Semi-supervised learning — Not all the data has labels, so we will train on labels and predict labels for others.
- Unsupervised learning
- Transfer Learning
Why are Deep Learning Use Cases Significant?
The importance of deep learning use cases lies in their capacity to significantly enhance the accuracy and effectiveness of a broad spectrum of applications. Deep learning algorithms prove invaluable for tasks that conventional algorithms find challenging, as they autonomously learn and make decisions by discerning patterns in the provided data. These use cases are pivotal for supporting a diverse array of applications:
- Image Classification: Deep learning algorithms, when trained on extensive labeled image databases, excel at categorizing new photos based on their content. For instance, they can identify the presence of a cat in a fresh image of a cat. Applications include automatically labeling images on social media and object identification in self-driving vehicle sensors.
- Speech Recognition: Deep learning finds practical applications in transcribing spoken words into written text. This is useful for tasks such as translating spoken words into written text or real-time language translation. It also facilitates the recognition of voice commands for operating smart home devices.
- Natural Language Processing: Deep learning’s ability to comprehend human language is harnessed in various applications, including text summarization and language translation. For example, a deep learning system trained on a substantial corpus of translated text can be used to translate new material. It can also automatically summarize lengthy articles by highlighting key points and for spam detection etc.
- Image Recognition: Deep learning proves beneficial for computer vision applications, allowing computers to recognize human faces and identify image contents. Major companies like Google, Facebook, and IBM utilize deep learning to achieve remarkable image recognition capabilities.
- Translation: Beyond understanding one language, deep learning systems can be trained to comprehend multiple languages and provide translations. Several vendors offer APIs with deep learning-based translation capabilities to facilitate cross-language communication.
- Recommendation Engines: Deep learning powers recommendation engines on platforms like Amazon and Netflix, offering users personalized suggestions based on their past usage patterns. These engines continuously improve over time, uncovering hidden correlations in user preferences.
- Analytics: Deep learning plays a crucial role in enhancing big data analytics, making predictive and prescriptive analytics more sophisticated and effective for businesses.
- Forecasting: Deep learning is widely applied in forecasting, predicting future events such as customer demand, supply chain challenges, and future earnings, contributing to strategic decision-making in enterprises.
What is TensorFlow and Why we use TensorFlow?
TensorFlow stands as a comprehensive platform for machine learning, enabling the swift development of deep learning code in Python that can efficiently run on GPU/TPU. Utilizing TensorFlow Hub allows seamless access to a plethora of prebuilt deep learning models. The platform offers a complete stack, encompassing tasks such as preprocessing data, modeling data, and deploying the model into your application. It offers below major features:
- Numeric computation based on multidimensional arrays (akin to NumPy.)
- GPU and distributed processing capabilities
- Automatic differentiation
- Construction, training, and exportation of models on prem or cloud environments
- Implement MLOps
What is a GPU/TPU?
Short for Graphics Processing Unit, GPUs are specialized hardware designed to enhance graphic rendering in various computer applications. They have the capability to process multiple data elements simultaneously and efficiently. GPUs find extensive use in training resource-intensive machine learning and deep learning applications, as well as being heavily utilized in the gaming industry.
Abbreviated for Tensor Processing Unit, TPUs are also specialized hardware employed to accelerate the training of machine learning models. However, they exhibit more application-specific characteristics when compared to GPUs. While GPUs demonstrate greater flexibility in handling irregular computations, TPUs are specifically optimized for efficiently processing large batches of Convolutional Neural Networks (CNNs), thanks to their specially designed Matrix Multiply Unit.
What is a Tensor?
Tensors are arrays with multiple dimensions, characterized by a uniform type referred to as a dtype. You can find a list of all supported dtypes at tf.dtypes. If you have experience with NumPy, tensors are somewhat analogous to np.arrays. Like Python strings, all tensors are immutable, meaning that you cannot modify the contents of a tensor; you can only create a new one.
In neural networks, tensors store both the data and the weights and biases. They are optimized for hardware acceleration, enabling neural networks to perform the necessary mathematical operations swiftly. Tensors facilitate efficient backpropagation through Automatic Differentiation, ensuring that the network can adapt and learn effectively.
Above is the high-level flow in TensorFlow, which is like what we learnt in Scikit-Learn. We will cover all these steps in series of blogs, but we have covered all the details on what is deep learning and how it can be achieved using neural networks, in future blogs we will cover basics regarding TensorFlow.