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☕ Java Machine Learning

Exploring the intersection of Java's robust ecosystem with machine learning algorithms, these books guide developers through building intelligent systems in one of the world's most widely deployed languages.
Java Cookbook
by Ian F. Darwin • 2001
Everyday Java challenges and the clean, working code that defeats them, from I/O to lambdas.
3.69
162
☕ Java Programming👨‍🍳 Recipe Cookbook🧩 Design Patterns
Java Cookbook Summary
Java Cookbook 91%
Everyday Java challenges and the clean, working code that defeats them, from I/O to lambdas.
by Ian F. Darwin 2001
3.69
162 ratings
☕ Java Programming👨‍🍳 Recipe Cookbook🧩 Design Patterns
Java Deep Learning Essentials
by Yusuke Sugomori • 2016
Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.
3.20
5
🧠 Deep Learning🔧 Practical Deep Learning👁️ Computer Vision
Java Deep Learning Essentials Summary
Java Deep Learning Essentials 91%
Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.
by Yusuke Sugomori 2016
3.20
5 ratings
🧠 Deep Learning🔧 Practical Deep Learning👁️ Computer Vision
Algorithms of the Intelligent Web
by Haralambos Marmanis • 2009
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
3.62
130
🤖 Machine Learning🌐 Web Applications🎯 Recommendation Systems
Algorithms of the Intelligent Web Summary
Algorithms of the Intelligent Web 91%
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
by Haralambos Marmanis 2009
3.62
130 ratings
🤖 Machine Learning🌐 Web Applications🎯 Recommendation Systems
Functional Programming in Java
by Venkat Subramaniam • 2013
Cleaner code, less boilerplate, effortless parallelism: the Java 8 lambda and streams playbook.
4.17
226
🧱 Java Development🔧 Functional Programming☕ Modern Java
Functional Programming in Java Summary
Functional Programming in Java 91%
Cleaner code, less boilerplate, effortless parallelism: the Java 8 lambda and streams playbook.
by Venkat Subramaniam 2013
4.17
226 ratings
🧱 Java Development🔧 Functional Programming☕ Modern Java
Head First Java
by Kathy Sierra • 2006
A Java guide that fits how your brain learns: puzzles, doodles, and stories that actually stick.
4.24
4k+
🍵 Java Language🏗️ Object-Oriented Design👁️ Visual Learning
Head First Java Summary
Head First Java 90%
A Java guide that fits how your brain learns: puzzles, doodles, and stories that actually stick.
by Kathy Sierra 2006
4.24
4k+ ratings
🍵 Java Language🏗️ Object-Oriented Design👁️ Visual Learning
Java 8 in Action
by Raoul-Gabriel Urma • 2014
Replace anonymous inner classes with lambdas, iterate with streams, and fix broken dates.
4.35
710
☕ Java Programming🧠 Functional Programming📈 Language Evolution
Java 8 in Action Summary
Java 8 in Action 90%
Replace anonymous inner classes with lambdas, iterate with streams, and fix broken dates.
by Raoul-Gabriel Urma 2014
4.35
710 ratings
☕ Java Programming🧠 Functional Programming📈 Language Evolution
The Hundred-Page Machine Learning Book
by Andriy Burkov • 2019
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
4.25
1k+
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
The Hundred-Page Machine Learning Book Summary
The Hundred-Page Machine Learning Book 90%
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
by Andriy Burkov 2019
4.25
1k+ ratings
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
Hands-On Machine Learning with Scikit-Learn and TensorFlow
by Aurélien Géron • 2017
How neural networks remember: training RNNs, LSTMs, and GRUs for tasks where order matters.
4.55
3k+
🧠 Deep Learning🔗 Neural Networks🛠️ Practical Ml
Hands-On Machine Learning with Scikit-Learn and TensorFlow Summary
Hands-On Machine Learning with Scikit-Learn and TensorFlow 90%
How neural networks remember: training RNNs, LSTMs, and GRUs for tasks where order matters.
by Aurélien Géron 2017
4.55
3k+ ratings
🧠 Deep Learning🔗 Neural Networks🛠️ Practical Ml
AI and Machine Learning for Coders
by Laurence Moroney • 2021
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
4.09
108
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
AI and Machine Learning for Coders Summary
AI and Machine Learning for Coders 90%
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
by Laurence Moroney 2021
4.09
108 ratings
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
Java Performance
by Scott Oaks • 2014
Fast Java has no magic switch: profiler first, then GC, JIT, threads, and memory.
4.39
319
🖥️ Jvm Performance⚙️ Java Internals🗑️ Garbage Collection
Java Performance Summary
Java Performance 90%
Fast Java has no magic switch: profiler first, then GC, JIT, threads, and memory.
by Scott Oaks 2014
4.39
319 ratings
🖥️ Jvm Performance⚙️ Java Internals🗑️ Garbage Collection
Functional Programming for Java Developers
by Dean Wampler • 2011
Immutability kills whole categories of concurrency bugs. The functional tools Java developers need.
3.27
126
🧑‍💻 Functional Programming⚡ Concurrent Programming☕ Java Development
Functional Programming for Java Developers Summary
Functional Programming for Java Developers 90%
Immutability kills whole categories of concurrency bugs. The functional tools Java developers need.
by Dean Wampler 2011
3.27
126 ratings
🧑‍💻 Functional Programming⚡ Concurrent Programming☕ Java Development
Machine Learning For Absolute Beginners
by Oliver Theobald • 2017
How machine learning algorithms think, from regression to neural networks, no math required.
4.12
474
🧠 Machine Learning🧹 Data Preparation⚖️ Model Evaluation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 90%
How machine learning algorithms think, from regression to neural networks, no math required.
by Oliver Theobald 2017
4.12
474 ratings
🧠 Machine Learning🧹 Data Preparation⚖️ Model Evaluation
Machine Learning For Absolute Beginners
by Oliver Theobald • 2018
A plain English path from zero to a working model: the algorithms, data prep, and code.
3.96
786
🤖 Machine Learning Basics🐍 Python For Data Science🧹 Data Preparation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 89%
A plain English path from zero to a working model: the algorithms, data prep, and code.
by Oliver Theobald 2018
3.96
786 ratings
🤖 Machine Learning Basics🐍 Python For Data Science🧹 Data Preparation
Java Concurrency in Practice
by Brian Goetz • 2005
Concurrency bugs come from shared mutable state. The Java tools and patterns that eliminate them.
4.48
3k+
🧵 Concurrent Programming🔒 Thread Safety🧶 Multi-Threading
Java Concurrency in Practice Summary
Java Concurrency in Practice 89%
Concurrency bugs come from shared mutable state. The Java tools and patterns that eliminate them.
by Brian Goetz 2005
4.48
3k+ ratings
🧵 Concurrent Programming🔒 Thread Safety🧶 Multi-Threading
Deep Learning with Python
by François Chollet • 2017
The Keras creator teaches you to build neural networks that see, read, and create in Python.
4.57
1k+
🧠 Deep Learning🐍 Python Programming🏋️ Model Training
Deep Learning with Python Summary
Deep Learning with Python 89%
The Keras creator teaches you to build neural networks that see, read, and create in Python.
by François Chollet 2017
4.57
1k+ ratings
🧠 Deep Learning🐍 Python Programming🏋️ Model Training
Kotlin in Action
by Dmitry Jemerov • 2016
Write less code, eliminate null errors, and keep every Java library. A Kotlin guide for developers.
4.41
603
🇰 Kotlin☕ Java🔬 Functional Programming
Kotlin in Action Summary
Kotlin in Action 89%
Write less code, eliminate null errors, and keep every Java library. A Kotlin guide for developers.
by Dmitry Jemerov 2016
4.41
603 ratings
🇰 Kotlin☕ Java🔬 Functional Programming
Neural Network for Beginners
by Sebastian Klaas • 2021
Stop treating neural networks as magic. Build them from scratch in Python, line by line.
5.00
1
🧠 Machine Learning🧬 Deep Learning🔗 Neural Networks
Neural Network for Beginners Summary
Neural Network for Beginners 89%
Stop treating neural networks as magic. Build them from scratch in Python, line by line.
by Sebastian Klaas 2021
5.00
1 ratings
🧠 Machine Learning🧬 Deep Learning🔗 Neural Networks
Python for Geeks
by Muhammad Asif • 2021
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
4.50
8
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
Python for Geeks Summary
Python for Geeks 89%
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
by Muhammad Asif 2021
4.50
8 ratings
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
Python Data Science Handbook
by Jake VanderPlas • 2016
Four libraries, one workflow: the Python path from raw data to validated predictions.
4.29
676
📊 Data Science🤖 Machine Learning🐍 Scientific Python
Python Data Science Handbook Summary
Python Data Science Handbook 89%
Four libraries, one workflow: the Python path from raw data to validated predictions.
by Jake VanderPlas 2016
4.29
676 ratings
📊 Data Science🤖 Machine Learning🐍 Scientific Python
Machine Learning with R
by Brett Lantz • 2015
Build the algorithms that catch spam, flag fraud, and recommend products: a hands-on guide in R.
4.20
312
💻 R Language🤖 Machine Learning📈 Predictive Modeling
Machine Learning with R Summary
Machine Learning with R 89%
Build the algorithms that catch spam, flag fraud, and recommend products: a hands-on guide in R.
by Brett Lantz 2015
4.20
312 ratings
💻 R Language🤖 Machine Learning📈 Predictive Modeling
Machine Learning
by Dr Ruchi Doshi • 2021
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
5.00
1
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Machine Learning Summary
Machine Learning 89%
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
by Dr Ruchi Doshi 2021
5.00
1 ratings
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Introduction to Machine Learning with Python
by Andreas C. Müller • 2015
Build models that generalize, not just memorize: a Python data scientist's guide.
4.33
600
🤖 Applied Machine Learning📊 Data Science🏷️ Supervised Learning
Introduction to Machine Learning with Python Summary
Introduction to Machine Learning with Python 89%
Build models that generalize, not just memorize: a Python data scientist's guide.
by Andreas C. Müller 2015
4.33
600 ratings
🤖 Applied Machine Learning📊 Data Science🏷️ Supervised Learning
AI and Machine Learning for On-Device Development
by Laurence Moroney • 2021
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
4.17
6
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
AI and Machine Learning for On-Device Development Summary
AI and Machine Learning for On-Device Development 89%
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
by Laurence Moroney 2021
4.17
6 ratings
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
Introduction To Machine Learning
by Ethem Alpaydin • 2004
The mathematical core of machine learning, taught without cutting corners: from Bayes to boosting.
3.78
251
🤖 Machine Learning📊 Statistical Learning🏷️ Supervised Learning
Introduction To Machine Learning Summary
Introduction To Machine Learning 89%
The mathematical core of machine learning, taught without cutting corners: from Bayes to boosting.
by Ethem Alpaydin 2004
3.78
251 ratings
🤖 Machine Learning📊 Statistical Learning🏷️ Supervised Learning
Data Structures and Problem Solving Using Java
by Mark Allen Weiss • 2009
Coding tricks lose to growth rates. The data structures and guarantees that survive at scale.
3.58
36
🏗️ Data Structures🧠 Algorithmic Thinking🕸️ Graph Algorithms
Data Structures and Problem Solving Using Java Summary
Data Structures and Problem Solving Using Java 89%
Coding tricks lose to growth rates. The data structures and guarantees that survive at scale.
by Mark Allen Weiss 2009
3.58
36 ratings
🏗️ Data Structures🧠 Algorithmic Thinking🕸️ Graph Algorithms
Machine Learning for Dummies
by John Paul Mueller • 2016
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
3.53
139
🧠 Machine Learning Basics📊 Data Science Basics🏷️ Supervised Learning
Machine Learning for Dummies Summary
Machine Learning for Dummies 89%
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
by John Paul Mueller 2016
3.53
139 ratings
🧠 Machine Learning Basics📊 Data Science Basics🏷️ Supervised Learning
Java to Kotlin
by Duncan McGregor • 2021
Migrating Java to Kotlin safely: functional refactoring that keeps production running.
4.58
33
☕ Java Developers🔄 Kotlin Migration🧠 Functional Programming
Java to Kotlin Summary
Java to Kotlin 89%
Migrating Java to Kotlin safely: functional refactoring that keeps production running.
by Duncan McGregor 2021
4.58
33 ratings
☕ Java Developers🔄 Kotlin Migration🧠 Functional Programming
Machine Learning
by Ethem Alpaydin • 2016
From spam filters to self-driving cars: the algorithms that learn from data, and what comes next.
3.62
1k+
🧠 Machine Learning🕸️ Neural Networks🔬 Deep Learning
Machine Learning Summary
Machine Learning 89%
From spam filters to self-driving cars: the algorithms that learn from data, and what comes next.
by Ethem Alpaydin 2016
3.62
1k+ ratings
🧠 Machine Learning🕸️ Neural Networks🔬 Deep Learning
Keras Reinforcement Learning Projects
by Giuseppe Ciaburro • 2018
Nine Keras projects that bring reinforcement learning from theory to working self-learning agents.
4.00
1
🤖 Reinforcement Learning🧠 Deep Learning🔗 Neural Networks
Keras Reinforcement Learning Projects Summary
Keras Reinforcement Learning Projects 89%
Nine Keras projects that bring reinforcement learning from theory to working self-learning agents.
by Giuseppe Ciaburro 2018
4.00
1 ratings
🤖 Reinforcement Learning🧠 Deep Learning🔗 Neural Networks
Low-Code AI
by Gwendolyn Stripling • 2023
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
4.33
6
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Low-Code AI Summary
Low-Code AI 89%
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
by Gwendolyn Stripling 2023
4.33
6 ratings
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Designing Machine Learning Systems
by Chip Huyen • 2022
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
4.45
1k+
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
Designing Machine Learning Systems Summary
Designing Machine Learning Systems 89%
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
by Chip Huyen 2022
4.45
1k+ ratings
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
Effective Java
by Joshua Bloch • 2001
The Java library's hidden contract with your objects: obey it, or spend your life debugging.
4.51
8k+
☕ Java Best Practices🔌 Api Design🧊 Immutable Objects
Effective Java Summary
Effective Java 88%
The Java library's hidden contract with your objects: obey it, or spend your life debugging.
by Joshua Bloch 2001
4.51
8k+ ratings
☕ Java Best Practices🔌 Api Design🧊 Immutable Objects
Natural Language Processing with Transformers
by Lewis Tunstall • 2022
Build NLP applications with the open-source stack that turned transformer research practical.
4.39
211
🗣️ Natural Language Processing🧠 Deep Learning🤖 Transformers
Natural Language Processing with Transformers Summary
Natural Language Processing with Transformers 88%
Build NLP applications with the open-source stack that turned transformer research practical.
by Lewis Tunstall 2022
4.39
211 ratings
🗣️ Natural Language Processing🧠 Deep Learning🤖 Transformers
Python Machine Learning Case Studies
by Danish Haroon • 2017
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
2.60
5
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Python Machine Learning Case Studies Summary
Python Machine Learning Case Studies 88%
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
by Danish Haroon 2017
2.60
5 ratings
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Learning Android
by Marko Gargenta • 2011
Android apps are built from four core components and a threading rule preventing sluggish UIs.
3.68
213
🤖 Android Programming🏛️ App Architecture📱 Mobile Ui Design
Learning Android Summary
Learning Android 88%
Android apps are built from four core components and a threading rule preventing sluggish UIs.
by Marko Gargenta 2011
3.68
213 ratings
🤖 Android Programming🏛️ App Architecture📱 Mobile Ui Design
Deep Learning
by John D. Kelleher • 2019
Deep learning isn't clever math. It's simple neurons, wired at scale, learning from their mistakes.
3.89
473
🔬 Deep Learning🧠 Neural Networks🤖 Machine Learning
Deep Learning Summary
Deep Learning 88%
Deep learning isn't clever math. It's simple neurons, wired at scale, learning from their mistakes.
by John D. Kelleher 2019
3.89
473 ratings
🔬 Deep Learning🧠 Neural Networks🤖 Machine Learning
Neural Networks, Fuzzy Logic And Genetic Algorithms
by S. Rajasekaran • 2004
Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.
4.21
151
🖥️ Soft Computing🧠 Neural Networks🌫️ Fuzzy Logic
Neural Networks, Fuzzy Logic And Genetic Algorithms Summary
Neural Networks, Fuzzy Logic And Genetic Algorithms 88%
Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.
by S. Rajasekaran 2004
4.21
151 ratings
🖥️ Soft Computing🧠 Neural Networks🌫️ Fuzzy Logic
Kotlin for Android Developers
by Antonio Leiva • 2016
Build an Android app while learning Kotlin: less boilerplate, null safety, and concise code.
3.85
183
📱 Android Development🔷 Kotlin📚 Project-Based Learning
Kotlin for Android Developers Summary
Kotlin for Android Developers 88%
Build an Android app while learning Kotlin: less boilerplate, null safety, and concise code.
by Antonio Leiva 2016
3.85
183 ratings
📱 Android Development🔷 Kotlin📚 Project-Based Learning
Machine Learning Simplified
by Andrew Wolf • 2022
Supervised learning, gently: pipeline, optimization, and the real reasons your model overfits.
4.87
193
🎯 Supervised Learning📚 Machine Learning Fundamentals📊 Statistical Learning
Machine Learning Simplified Summary
Machine Learning Simplified 88%
Supervised learning, gently: pipeline, optimization, and the real reasons your model overfits.
by Andrew Wolf 2022
4.87
193 ratings
🎯 Supervised Learning📚 Machine Learning Fundamentals📊 Statistical Learning
Data Structures and Algorithms Made Easy in Java
by Narasimha Karumanchi • 2011
Data structures and algorithms, taught problem by problem in Java. Built for the interview.
4.17
471
🧱 Data Structures🔧 Algorithm Design💼 Coding Interviews
Data Structures and Algorithms Made Easy in Java Summary
Data Structures and Algorithms Made Easy in Java 88%
Data structures and algorithms, taught problem by problem in Java. Built for the interview.
by Narasimha Karumanchi 2011
4.17
471 ratings
🧱 Data Structures🔧 Algorithm Design💼 Coding Interviews
AI for Data Science
by Zacharias Voulgaris • 2018
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
4.55
20
📊 Data Science🧠 Deep Learning⚙️ Optimization Algorithms
AI for Data Science Summary
AI for Data Science 88%
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
by Zacharias Voulgaris 2018
4.55
20 ratings
📊 Data Science🧠 Deep Learning⚙️ Optimization Algorithms
Data Science from Scratch
by Joel Grus • 2015
Real understanding comes from building algorithms yourself, not calling libraries others wrote.
3.90
1k+
🐍 Python For Data Science⚙️ Algorithm Implementation📊 Statistical Thinking
Data Science from Scratch Summary
Data Science from Scratch 88%
Real understanding comes from building algorithms yourself, not calling libraries others wrote.
by Joel Grus 2015
3.90
1k+ ratings
🐍 Python For Data Science⚙️ Algorithm Implementation📊 Statistical Thinking
Practical Time Series Analysis
by Aileen Nielsen • 2019
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
3.77
64
📈 Time Series Analysis🔧 Feature Engineering🧹 Data Preparation
Practical Time Series Analysis Summary
Practical Time Series Analysis 88%
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
by Aileen Nielsen 2019
3.77
64 ratings
📈 Time Series Analysis🔧 Feature Engineering🧹 Data Preparation
Deep Learning Design Patterns
by Andrew Ferlitsch • 2021
Data shift crushed your model? Simple design patterns push accuracy from 10% to 98%.
4.67
3
🧠 Deep Learning👁️ Computer Vision🔄 Data Augmentation
Deep Learning Design Patterns Summary
Deep Learning Design Patterns 88%
Data shift crushed your model? Simple design patterns push accuracy from 10% to 98%.
by Andrew Ferlitsch 2021
4.67
3 ratings
🧠 Deep Learning👁️ Computer Vision🔄 Data Augmentation
The Machine Learning Solutions Architect Handbook
by David Ping • 2022
Most ML models never make it to production. The architecture playbook for the ones that do.
4.06
16
🏢 Enterprise Ml🏗️ Solution Architecture🔄 Ml Lifecycle
The Machine Learning Solutions Architect Handbook Summary
The Machine Learning Solutions Architect Handbook 88%
Most ML models never make it to production. The architecture playbook for the ones that do.
by David Ping 2022
4.06
16 ratings
🏢 Enterprise Ml🏗️ Solution Architecture🔄 Ml Lifecycle
Effective Unit Testing
by Lasse Koskela • 2012
A slow test suite kills productivity. The craft rules for fast, trustworthy unit tests.
3.80
118
🧪 Unit Testing☕️ Java Development🧩 Testable Design
Effective Unit Testing Summary
Effective Unit Testing 88%
A slow test suite kills productivity. The craft rules for fast, trustworthy unit tests.
by Lasse Koskela 2012
3.80
118 ratings
🧪 Unit Testing☕️ Java Development🧩 Testable Design
Artificial Intelligence and Machine Learning for Business
by Steven Finlay • 2018
ML projects fail for business reasons, not technical ones. The rules for getting it right.
4.14
207
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
Artificial Intelligence and Machine Learning for Business Summary
Artificial Intelligence and Machine Learning for Business 88%
ML projects fail for business reasons, not technical ones. The rules for getting it right.
by Steven Finlay 2018
4.14
207 ratings
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
Introduction to Computation and Programming Using Python
by John V. Guttag • 2013
A computer calculates and remembers; everything beyond that is what you build with code.
4.22
500
🧑‍💻 Introductory Programming🧠 Computational Thinking🐍 Python For Beginners
Introduction to Computation and Programming Using Python Summary
Introduction to Computation and Programming Using Python 87%
A computer calculates and remembers; everything beyond that is what you build with code.
by John V. Guttag 2013
4.22
500 ratings
🧑‍💻 Introductory Programming🧠 Computational Thinking🐍 Python For Beginners
The Little Book of Deep Learning
by François Fleuret • 2023
The machinery of deep learning, compressed: gradient descent, backprop, and attention.
4.32
151
🧠 Deep Learning🔧 Model Architectures⚡ Hardware Acceleration
The Little Book of Deep Learning Summary
The Little Book of Deep Learning 87%
The machinery of deep learning, compressed: gradient descent, backprop, and attention.
by François Fleuret 2023
4.32
151 ratings
🧠 Deep Learning🔧 Model Architectures⚡ Hardware Acceleration
Python Programming
by John Zelle • 2003
A Python-first CS textbook that teaches what computation can do, not just how to code.
4.02
483
📘 Introductory Programming🐍 Python Programming🧮 Algorithm Fundamentals
Python Programming Summary
Python Programming 87%
A Python-first CS textbook that teaches what computation can do, not just how to code.
by John Zelle 2003
4.02
483 ratings
📘 Introductory Programming🐍 Python Programming🧮 Algorithm Fundamentals
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⭐
⭐
⭐
We have a special gift for you
Open
38% OFF
DISCOUNT FOR YOU
⭐
⭐
⭐
$79.99
$49.99/year
only $4.16 per month
Continue
2 taps to start, super easy to cancel