#learn tensorflow

[feitgemel]

Discover how to build a CNN model for skin melanoma classification using over 20,000 images of skin lesions

We'll begin by diving into data preparation, where we will organize, clean, and prepare the data form the classification model.

Next, we will walk you through the process of build and train convolutional neural network (CNN) model. We'll explain how to build the layers, and optimize the model.

Finally, we will test the model on a new fresh image and challenge our model.

Check out our tutorial here : https://youtu.be/RDgDVdLrmcs

Enjoy

Eran

#Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #SkinMelanoma #melonomaclassification

[allyourchoice]

Top 10 Python libraries for 2025

Top 10 Python Libraries You Should Master in 2025

Python has remained one of the top programming languages over the years because of its ease, adaptability, and large community. In 2025, Python is still the leading language across different fields, ranging from web design to data science and machine learning. To be competitive and productive in your Python projects, mastering the correct libraries is critical. Here's a list of the top 10 Python libraries you should learn in 2025 to level up your coding game. 1. TensorFlow Use Case: Machine Learning & Deep Learning Overview: TensorFlow, created by Google, is one of the leading machine learning and deep learning libraries. It's utilized for creating and training deep neural networks and is extensively used in many applications like image recognition, natural language processing, and autonomous systems. Why Master It? With the advent of AI and deep learning in 2025, TensorFlow is a library that must be mastered. It's extremely flexible, accommodates scalable machine learning tasks, and enjoys strong community support and tutorials. 2. Pandas Use Case: Data Manipulation & Analysis Overview: Pandas is a must-have library for data manipulation and analysis. It offers robust tools for data cleaning, analysis, and visualization through its DataFrame and Series data structures. It integrates perfectly with data from various sources such as CSV, Excel, SQL databases, and others. Why Master It? Data analytics and science remain key areas in 2025. Pandas is central to data wrangling and analysis and, thus, a must-have tool for anyone handling data. 3. Flask Use Case: Web Development (Micro-Framework) Overview: Flask is a simple, lightweight web framework in Python used for quick and efficient development of web applications. It's bare-bones, having flexibility for developers who desire greater control over their applications. Why Master It? Flask will still be a favorite for microservices and APIs in 2025. It's ideal for those who like the modular way of developing applications, so it's great for fast and scalable web development. 4. NumPy Use Case: Scientific Computing & Numerical Analysis Overview: NumPy is the backbone of numerical computing in Python. It supports large multi-dimensional arrays and matrices and has an enormous library of high-level mathematical functions to work on these arrays. Why Master It? In 2025, numerical computing will still be critical to data science, finance, machine learning, and engineering tasks. NumPy mastering is vital to efficient mathematical operations and data manipulation in scientific computing. 5. PyTorch Use Case: Machine Learning & Deep Learning Overview: PyTorch is a deep learning framework created by Facebook's AI Research lab and has quickly become popular because it is flexible, easy to use, and has a large community of developers. It's utilized for creating sophisticated neural networks and is also famous for having a dynamic computation graph. Why Master It? PyTorch is a top pick for machine learning practitioners in 2025, particularly for research and experimentation. It's simple yet powerful, and that makes it a great fit for leading-edge AI development. 6. Matplotlib Use Case: Data Visualization Overview: Matplotlib is the first choice library to create static, animated, and interactive visualizations in Python. It's applied for plotting data, graph creation, and chart construction that facilitates making sense of big datasets. Why Master It? Data visualization is crucial to the interpretation and representation of insights. Learning Matplotlib will enable you to effectively communicate your data discoveries, making it an essential for data analysts, scientists, and anyone who works with data in 2025. 7. Scikit-learn Use Case: Machine Learning Overview: Scikit-learn is among the most widely used machine learning libraries, providing simple-to-use tools for classification, regression, clustering, and dimensionality reduction. It can handle both supervised and unsupervised learning and is compatible with other scientific libraries such as NumPy and SciPy. Why Master It? In 2025, Scikit-learn continues to be a robust, easy-to-use library for creating and deploying machine learning models. Its simplicity and thoroughly documented functionality make it perfect for both beginners and experts in data science and machine learning. 8. Keras Use Case: Deep Learning Overview: Keras is an open source library that is an interface for TensorFlow, enabling users to make deep learning model creation and training more convenient. Keras uses a high-level API that allows it to design neural networks and sophisticated models without complexities. Why Master It With the increased significance of deep learning, Keras will be a go-to choice in 2025. It makes designing neural networks easier and is a great tool for those who need to prototype deep learning models very quickly without delving into difficult code. 9. Django Use Case: Web Development (Full-Stack Framework) Overview: Django is a Python web framework for rapid development and clean, pragmatic design. It also has built-in features such as authentication, an admin interface, and an ORM (Object-Relational Mapping) that make it suitable for developing strong web applications. Why Master It? In 2025, Django remains a top choice among frameworks for creating scalable, secure, and easy-to-maintain web applications. To work in full-stack web development, you must be proficient in Django. 10. Seaborn Use Case: Data Visualization Overview: Seaborn is a Python data visualization library based on Matplotlib. Seaborn simplifies the development of attractive and informative statistical visualizations. Seaborn gives a high-level interface for making beautiful and informative data visualizations. Why Master It? Seaborn will still be useful in 2025 for people working on depicting sophisticated statistical data. It is ideal for data analysis due to its inclusion with Pandas and NumPy, and rich color palettes and styles will make your plots look more visually appealing. Conclusion As we enter 2025, these top 10 Python libraries—spanning from AI and machine learning libraries such as TensorFlow and PyTorch to web frameworks such as Flask and Django—will inform the future of software development, data science, AI, and web applications. Regardless of your level of expertise—beginner or experienced Python developer—becoming a master of these libraries will give you the knowledge necessary to remain competitive and effective in the modern tech world. Read the full article

[tech-by-tehub]

Frameworks de IA: Comparativo entre TensorFlow e PyTorch

Comparativo entre TensorFlow e PyTorch: Você já se perguntou qual é o framework de IA mais adequado para o seu próximo projeto de inteligência artificial? A escolha do framework certo pode ser crucial para o sucesso do seu projeto. Com tantas opções disponíveis, é fundamental entender as diferenças entre os principais frameworks de IA. Neste artigo, vamos explorar as principais diferenças entre…

[topsinfosolutions]

TensorFlow Lite is a lightweight version of TensorFlow, designed to bring machine learning (ML) models to mobile, embedded, and edge devices. It enables fast, efficient, and offline AI experiences—without depending on cloud servers.

[freddynossa]

Plataformas de Aprendizaje Automático: Las Herramientas que Impulsan la Revolución de la IA

  El aprendizaje automático (Machine Learning) se ha convertido en uno de los campos más dinámicos y transformadores de la tecnología moderna. Detrás de cada avance en inteligencia artificial, desde el reconocimiento facial hasta los vehículos autónomos, se encuentran potentes plataformas de software que permiten a desarrolladores e investigadores crear, entrenar y desplegar modelos de IA cada…

[manmishra]

Quantum Computing and Artificial Intelligence: The Future of Technology

Discover how quantum computing is revolutionizing artificial intelligence. Learn about Quantum AI, its applications, quantum algorithms, and how it can accelerate AGI development. Explore the future of AI powered by quantum computing.

Quantum computing and artificial intelligence (AI) are two of the most revolutionary technological advancements in modern times. AI has already made significant progress using classical computers, but its potential is hindered by the computational limits of traditional computing systems. Quantum computing, with its immense processing power, is expected to drive AI into new frontiers, enabling…

[lukmanansari]

Belajar Machine Learning Menggunakan TensorFlow untuk Pemula

Hai Semuanya Machine Learning (ML) atau Pembelajaran Mesin merupakan cabang dari kecerdasan buatan (AI) yang memungkinkan sistem untuk belajar dari data, mengidentifikasi pola, dan membuat keputusan tanpa diprogram secara eksplisit. Dengan semakin berkembangnya teknologi, Machine Learning menjadi keterampilan yang sangat dibutuhkan dalam banyak industri, dari keuangan hingga kesehatan. Bagi…

[2ribu]

Peran Alat Pembelajaran Mesin dalam Meningkatkan Kemampuan AI di 2025

Pembelajaran mesin (machine learning) adalah cabang dari kecerdasan buatan (AI) yang memungkinkan sistem untuk belajar dan meningkatkan performa mereka tanpa pemrograman eksplisit. Dalam beberapa tahun terakhir, perkembangan pembelajaran mesin telah menjadi pendorong utama kemajuan AI. Pada tahun 2025, peran alat pembelajaran mesin semakin signifikan dalam meningkatkan kemampuan AI, baik dalam…

[mp3monsterme]

Fluent Bit and AI: Unlocking Machine Learning Potential

These days, everywhere you look, there are references to Generative AI, to the point that what have Fluent Bit and GenAI got to do with each other? GenAI has the potential to help with observability, but it also needs observation to measure its performance, whether it is being abused, etc. You may recall a few years back that Microsoft was trailing new AI features for Bing, and after only having…

[feitgemel]

🎣 Classify Fish Images Using MobileNetV2 & TensorFlow 🧠

In this hands-on video, I’ll show you how I built a deep learning model that can classify 9 different species of fish using MobileNetV2 and TensorFlow 2.10 — all trained on a real Kaggle dataset! From dataset splitting to live predictions with OpenCV, this tutorial covers the entire image classification pipeline step-by-step.

🚀 What you’ll learn:

How to preprocess & split image datasets

How to use ImageDataGenerator for clean input pipelines

How to customize MobileNetV2 for your own dataset

How to freeze layers, fine-tune, and save your model

How to run predictions with OpenCV overlays!

You can find link for the code in the blog: https://eranfeit.net/how-to-actually-fine-tune-mobilenetv2-classify-9-fish-species/

You can find more tutorials, and join my newsletter here : https://eranfeit.net/

👉 Watch the full tutorial here: https://youtu.be/9FMVlhOGDoo

Enjoy

Eran

#Python #ImageClassification #MobileNetV2

[engenhariadesoftware]

Explorando o TensorFlow: O Framework que Revolucionou o Machine Learning

Introdução ao TensorFlow O avanço da inteligência artificial (IA) e do aprendizado de máquina (Machine Learning) revolucionou diversas indústrias, como saúde, finanças, transporte e entretenimento. Nesse cenário, o TensorFlow, um framework de código aberto desenvolvido pelo Google, emerge como uma das ferramentas mais poderosas e amplamente utilizadas por desenvolvedores e pesquisadores para…

[justposting1]

Top AI Tools to Start Your Training in 2024

Empower Your AI Journey with Beginner-Friendly Platforms Like TensorFlow, PyTorch, and Google Colab The rapid advancements in artificial intelligence (AI) have transformed the way we work, live, and learn. For aspiring AI enthusiasts, diving into this exciting field requires a combination of theoretical understanding and hands-on experience. Fortunately, the right tools can make the learning…

[mitsde123]

Top Machine Learning Frameworks to Watch in 2024: TensorFlow, PyTorch, and Beyond

As machine learning continues to revolutionize industries, choosing the right framework is crucial for building robust, scalable, and efficient models. In 2024, several machine learning frameworks are leading the pack, each with unique features and capabilities that cater to different needs. This blog explores the top frameworks, including TensorFlow, PyTorch, and others, and how they compare to one another. Additionally, we’ll discuss how the MIT School of Distance Education (MITSDE) can help you master these frameworks through their comprehensive courses.

[ingoampt]

Day 17 _ Hyperparameter Tuning with Keras Tuner

Hyperparameter Tuning with Keras Tuner A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner Introduction In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. Keras Tuner is a powerful library that…

[itechburner]

What is TensorFlow? Understanding This Machine Learning Library

In this developing era of machine learning and deep learning, a question always arises: what is TensorFlow? It is a free to use library for artificial intelligence and ML. It can be used to perform various tasks but is specifically focused on integration and training of deep neural networks. It was developed in Google by the Google Brain team in 2015. 

In the starting of 2011, Google released an updated version with various features. Since then, it has received a huge demand in the industry for its excellent features. Considering its huge popularity, people always ask "what does TensorFlow do?" This article gives the proper understanding of this library along with its benefits and applications.

What is TensorFlow?

It is an open-source library introduced by Google primarily for deep learning operations. It was firstly developed to perform huge numerical computations, but not for deep learning applications. Now it supports numerical computations for various workloads, such as ML, DL and other predictive and statistical analysis.  

It collects data in the form of multi dimensional arrays of higher dimensions called tensors. These arrays are very convenient and helpful to collect and store a huge amount of data. This tool works according to data flow graphs that have edges and nodes. It is very simple to execute its code in a distributed manner among a cluster of computers.

How Does TensorFlow Work?

This library enables users to create dataflow graphs for a better representation of data flow in graphs. The graph has two factors: nodes and edges. Nodes represent a mathematical operation and the connection between nodes is called edge. This process takes inputs in the form of a multidimensional array. Users can also create a flowchart of operations that has to perform on these inputs.   

What is TensorFlow in Machine Learning?

What is TensorFlow in machine learning? It is an open-source machine learning framework. It is mostly used in developing and deploying ML models. Its demand in this field is due to its excellent flexibility. It helps to implement a variety of algorithms to perform operations. These operations includes:

Robotics

Healthcare

Fraud Detection

Generative Models

Speech Recognition

Reinforcement Learning

Recommendation Systems

Natural Language Processing (NLP)

Image Recognition and Classification

Time Series Analysis and Forecasting

Components of TensorFlow

The working of this tool can be easily understood by breaking it into its components. It can be divided into the following factors: 

Tensor 

The name TensorFlow is borrowed from its main framework, “Tensor”. A tensor is a vector of a n-dimensional matrix that demonstrates all kinds of data. All values in tensor are similar in data types with an informed shape. The shape of the data represents the dimension of the matrix. It can be generated by inputs or results of the process. 

Graphs

This tool mostly works on graph framework. The chart collects and describes all the computations completed during the process. It can run on multiple CPUs or GPUs and mobile operating systems. The portability of the graph allows it to conserve the computations for current or later use. All of the computation is executed by connecting tensors together.

For instance, consider an expression, such as:  a= (b+c)*(c+2)

This function can be break into components as: d=b+c, e=c+2, a=d*e

Graphical representation of the expression -

Session

A session is used to exclude the operation out of the graph. It helps to feed the graph with the tensor value. Inside a session, an operation must run in order to create an output. It is also used to evaluate the nodes. Here is an example of session:

Features of TensorFlow

This tool has an interactive multi-platform programming interface. It is more reliable and scalable compared to other DL platforms. The following features proves the popularity of this library:

Flexible

Open Source

Easily Trainable

Feature Columns

Large Community

Layered Components

Responsive Construct

Visualizer (with TensorBoard)

Parallel Neural Network Training

Availability of Statistical Distributions

Applications of TensorFlow

Many newcomers to the field of artificial intelligence often ask, 'What does TensorFlow do?’ It is an open-source platform designed for machine learning and DL operations. Here are some the applications of this library- 

1. Voice Recognition

It is one of the most popular use cases of this library. It is built on neural networks. These networks are capable of understanding voice signals if they have a proper input data feed. It is used for voice search, sentimental analysis, voice recognition and understanding audio signals.

The use case is widely popular in smartphone manufactures and mobile OS developers. This is used for voice assistance, such as Apple’s Siri, Microsoft Cortana and Google Assistance. It is also used in speech-to-text applications to convert audios into texts.   

2. Image Recognition

This use case is majorly used in social media and smartphones. Image recognition, image search, motion detection, computer vision and image clustering are its common usage. Google Lens and Meta’s deep face are examples of image recognition technology. This deep learning method can identify an object in an image never seen before. 

Healthcare industries are also using image recognition for quick diagnosis. TensorFlow algorithms help to recognise patterns and process data faster than humans. This procedure can detect illnesses and health issues faster than ever.    

3. Recommendation  

Recommendation is another method used today to form patterns and data forecasting. It helps to derive meaningful statistics along with recommended actions. It is used in various leading companies, such as Netflix, Amazon, Google etc. These applications always suggest the product according to customer preferences. 

4. Video Detection

These algorithms can also be used in video data. This is used in real-time threat detection, motion detection, gaming and security. NASA is using this technology to build a system for object clustering. It can help to predict and classify NEOs (Near Earth Objects) like orbits and asteroids. 

5. Text-Based Applications 

Text-based applications are also a popular use case of this library. Sentiment analysis, threat detection, social media, and fraud detection are some of the basic examples. Language detection and translation are other use cases of this tool.  Various companies like Google, AirBnb, eBay, Intel, DropBox, DeepMind, Airbus, CEVA, etc are using this library.

Final Words

This article has explained 'what is tensor flow'. It is a powerful open-source tool for machine learning and deep learning. It helps to create deep neural networks to support diverse applications like robotics, healthcare, fraud detection, etc. It is also used to perform large numerical computations. It provides data flow graphs to process multi-dimensional arrays called tensors. You can learn TensorFlow and get TensorFlow Certification.

Its components, such as tensors, graphs and sessions, helps in computation across CPUs, GPUs, and mobile devices. It has various features including flexibility, ease of training and extensive community support. It provides robust abilities, such as parallel neural network training and visualizations with TensorBoard. This makes it a cornerstone tool in the AI and ML landscape.