Introduction
What is Deep Learning?
Understanding the core ideas of deep learning is crucial before working with deep learning libraries. Deep learning is a branch of machine learning that focuses on teaching artificial neural networks to recognize images, understand natural language, and carry out other functions. These networks can learn complex features and patterns from data because of their depth, which is attained through the use of numerous layers.
A popular subset of a broader class of machine learning methods is called deep learning. More specifically, Deep Learning is used to dynamic databases. Being a relatively new phrase, some professionals who are considering entering the business or are currently working in it may find the vast array of instruments available intimidating.
If you want to explore more about Deep Learning and its trends in 2024, Please refer to this Blog: Deep Learning
Why Deep Learning is Important?
The value of deep learning is found in its ability to solve a wide range of issues that are too complex or unattainable for human experts or conventional algorithms. It can manage complicated and huge data sets that include text, audio, video, photos, and more.
It can also learn from unstructured or unlabelled data, so it doesn’t need supervision or human input to retrieve relevant information. Furthermore, Deep Learning may be trained to do activities that are incomprehensible for humans, including creating realistic visuals, making music, or playing video games. As a result, technology becomes essential to our daily life in several ways. The following are some of the factors that make deep learning significant:
Automatic Feature Learning
A fundamental advantage of Deep Learning is that it can learn features from data on its own, eliminating the requirement for manual feature design. This is especially useful for jobs that need hard-to-specify features, such as picture recognition, natural language processing, and speech interpretation. Some examples of automatic feature learning via Deep Learning algorithms include:
- Image Recognition: Deep algorithms can use Convolutional Neural Networks (CNNs) to learn features from images, recognize faces, objects, scenes, and actions in images and videos, segment images into regions, reconstruct 3D models from 2D images, and synthesize realistic images from sketches or text.
- Natural language processing (NLP): Deep Learning algorithms can extract features from text using Recurrent Neural Networks (RNNs), which can generate realistic text and images, as well as chatbots.
- Speech Recognition: Deep Learning algorithms have excelled in speech recognition tasks such as transcribing speech to text and translating speech into many languages.
Top 6 Deep learning Libraries
- Tensorflow
What is Tensorflow?
An open-source symbolic math package for machine learning is called TensorFlow. Neural networks serve as its foundation. It focuses mostly on differentiable programming and data flow.
The operations that neural networks carry out on multidimensional data arrays, or tensors, are the source of the moniker TensorFlow. Additionally, tensors are algebraic objects that characterize the link between sets of vector space-related algebraic objects.
Features:
- Flexibility: A range of deployment platforms, such as CPUs, GPUs, and TPUs, are supported by TensorFlow.
- High-level APIs: TensorFlow makes the process of developing models easier by offering high-level APIs like Keras.
- Rich ecosystem: TensorFlow comes with TensorFlow.js for browser-based apps and TensorFlow Lite for mobile devices.
Utilization
Product-based businesses like Airbnb, Airbus, PayPal, VSCO, Twitter, and so on benefit from it since it provides an excellent model.
The ability to see model parameters, gradients, and performance is provided by TensorFlow as a Tensorboard feature. TensorBoard is first and foremost a web-based visualization tool.
In order to create machine learning models, we also utilize TensorFlow. It also permits the deployment of machine learning models in production mode on several platforms, including browsers, devices, and on-premises or cloud environments.
Installation
- Tensorflow operates on both GPUs and CPUs.
- The pip command, “pip3 install <tensorflow wheel file path> or pip3 install tensorflow or pip3 install tensorflow-gpu,” can be used to install TensorFlow.
- Using the conda command, “conda install tensorflow,” we can also install TensorFlow in Anaconda.
- The CUDA toolbox must initially be installed in order to use TensorFlow on GPUs.
- The most recent versions of scipy and numpy must be installed before TensorFlow can be installed.
- Importing Module
Applications
As with DeepDream, its primary usage is in the creation of “Automated Automatic image annotation (or automatic image tagging) software.” With this software, a digital image’s metadata—that is, information about other data—is automatically assigned by the computer system in the form of keywords.
For Example: Implementation of Tensorflow:
Computer vision is utilized extensively in image retrieval systems. They also assist in finding and arranging interesting photos from a database.
- PyTorch
What is PyTorch?
Based on the Torch framework, PyTorch is an open-source machine learning and deep learning library. It was first created by Facebook’s AI Research lab (FAIR), became well-known due to its dynamic computing graph, which improved its usability for researchers.
The word “torch” in PyTorch refers to the torch library, while “py” stands for Python. Python does not directly use the Torch library. Consequently, Facebook has developed PyTorch, an expanded version of the Torch library. This is written in Python.
Torch is a scientific computing framework and open-source machine learning library. It is utilized as LuaJIT (a scripting language) for the Lua programming language. It offers a large selection of deep learning algorithms.
An adaptable Tensor or N-dimensional array is offered by the torch. It can do standard operations such as cloning, sharing storage, resizing, indexing, slicing, transposing, and type-casting.
Features:
- Dynamic Computational Graph: PyTorch’s dynamic computation graph gives debugging and model construction additional freedom.
- TorchScript: Facilitates the smooth transition for deployment from eager execution to graph mode.
- Libtorch: a C++ front-end for PyTorch that makes it possible to integrate with programs that don’t use Python.
Utilization
Many large corporations use PyTorch for various projects, including SparkCognition, IBM, JPMorgan Chase, Comcast, and Amgen.
There is a gentle learning curve in PyTorch. It also includes many NLP, machine learning, and computer vision capabilities. It has thus gained popularity in the data science and machine learning industries. We can state that it is beginner-friendly because it is simpler than other machine learning packages.
Installation
- Installing all dependencies, including the most recent pip version, setup tools, numpy, and scipy, is necessary before we can install PyTorch.
- Since PyTorch can operate with or without the CUDA tools, CUDA installation is required.
- Installing PyTorch is most straightforward when using Anaconda since it comes with all the necessary dependencies.
- The pip3 command, “pip3 install <wheel file path of pytorch>,” can be used to install PyTorch. Alternatively with “conda install <PyTorch path>” when using Anaconda. On the PyTorch website, we select the programming language to obtain the PyTorch path for Anaconda.
Applications
Applications related to computer vision and natural language processing are its primary uses.
- Keras
What is Keras?
Numerous functions in Keras are used to construct neural network building components. These building blocks resemble optimizers, activation functions, layers, and objectives. Both text and image datasets are supported. It writes simple deep neural network code for them as a result. It was created in March 2015 by François Chollet and is written in Python.
For recurrent and convolutional neural networks, Keras performs several tasks. Common utility layers supported by Keras include batch normalization, pooling, and dropout.
Features:
- User-Friendly: Keras is renowned for having an intuitive user interface that makes it simple to rapidly develop prototypes and construct models.
- Modularity: Using building elements like layers and optimizers, models can be built in a modular fashion.
- Support for Multiple Backends: Although Keras is currently mainly linked with TensorFlow, it can also operate on top of other deep learning libraries like Theano.
Utilization
Numerous functions in Keras are used to construct neural network building components. These building blocks resemble optimizers, activation functions, layers, and objectives. Both text and image datasets are supported. It writes simple deep neural network code for them as a result.
For recurrent and convolutional neural networks, Keras performs several tasks. Common utility layers supported by Keras include batch normalization, pooling, and dropout.
Installation
- We must install required libraries like numpy, scipy, and Theano before we can install Keras.
- Using the pip command, we can install Keras in Python: pip install Keras.
- With the command “conda install Keras,” we can also install Keras in Anaconda.
Applications
To build deep learning models, we utilize Keras. Additionally, we employ them for fine-tuning, feature extraction, and prediction.
- Theano
What is Theano?
An open-source library called Theano is used to do quick numerical computations. It is an enhanced compiler for mathematical expression definition, optimization, manipulation, and computation.
Theano mostly uses Numpy to transform your structure into incredibly well-structured code. Theano uses a symbolic form for its syntax. For this reason, even novice programmers can easily grasp and utilize it.
All of the expressions ought to be defined in the abstract, compiled, and then used in computations. When working with logarithmic and exponential functions, Theano automatically prevents errors and exceptions.
Features:
- Symbolic Mathematical Computations: Theano gives users the ability to effectively define, optimize, and assess mathematical expressions.
- GPU Acceleration: Theano, which is designed to maximize GPU utilization, can dramatically accelerate deep learning model training.
Utilization
Multi-dimensional or matrix-valued arrays are an efficient way to implement Theano. Theano uses the NumPy-Esque syntax to express computations. Moreover, Theano is the result of combining Numpy and Sympy.
Installation
- Theano utilizes both GPUs and CPUs. In comparison to CPU theano, GPU theano is faster.
- Using the pip command, type “pip install theano” to install Theano.
- Additionally, by use the conda command “conda install theano,” we may install it in an Anaconda.
Applications
- Theano is utilized in Deep Learning Projects for Scientific Computing. It generates wrapper libraries or Deep Learning models that we might utilize to streamline the procedure.
- In Deep Learning, we use it to manage the computational portion of complex neural network algorithms.
- TFLearn
What is TFLearn?
TensorFlow is used with TFLearn. Developed on top of TensorFlow, it is a transparent and flexible deep learning library. It is made to be completely transparent and compatible with TensorFlow, but also to offer a higher-level API to speed up and simplify the experiments.
TFLearn offers simple concepts for creating highly modular network layers with embedded optimizers and several metrics. Thus, it can be said to be simple to use and comprehend. Its weights, gradients, and activations graph display characteristics are visually appealing. It also offers a plethora of helpful features for training the integrated tensors.
Installation
- After installing TensorFlow, we may install TFLearn. It utilizes TensorFlow.
- The pip command, “pip3 install tflearn,” can be used to install it.
- With the commands “conda install pip” and “pip install tflearn,” we can also install it in Anaconda.
Applications
They are used by the majority of deep learning and AI models.
- NLTK
What is NLTK?
Natural Language Toolkit is referred to as NLTK. For writing Python apps, we utilize it. In order to be used in statistical natural language processing, these Python scripts process data in human languages.
Text processing-related functions and libraries are part of NLTK. Thus, it is included in libraries for word tokenization, tagging, dependency parsing, stemming, chunking, and classification. Put differently, NLTK can be thought of as a collection of numerous machine learning libraries.
Features:
- Resources and Corpora: A vast array of lexical resources and language corpora are accessible with NLTK. It is an invaluable tool for linguistic research and experimentation because of these resources, which include text collections, lexicons, and annotated corpora.
- Tokenization: Tokenization techniques provided by NLTK are effective in dividing text into individual words or sentences. A vital stage in natural language processing is tokenization, which is handled by NLTK’s tokenizers for a variety of linguistic problems.
- Stop terms: A collection of frequently used stopwords for several languages is included in NLTK. Stopwords are words that are frequently excluded from text analysis because they are frequently used and have little meaning.
Utilization
For jobs involving natural language processing, we mostly employ NLTK. These include of named entity recognition, language modeling, and neural machine translation. It provides an n-gram synonym bank called wordnet.
It is also beneficial for research, teaching, and computational linguistics. That is to say, it can be used by linguists, educators, students, engineers, researchers, and industrial users.
Installation
- The pip command, pip3 install nltk, can be used to install NLTK in Python.
- With the command “conda install nltk,” we can also install NLTK in Anaconda.
Applications
It facilitates the computer’s ability to read, comprehend, and analyze written content. Furthermore, we process text using it.