“…Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. …”

“…If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion…”

“…Understand how state-of-the-art NLP models work Learn the tools of the trade, including frameworks popular today Perform NLP tasks such as text classification, semantic search, and reading comprehension Solve problems using new models like transformers and techniques such as transfer learning Build NLP models from scratch with performance comparable or superior to out-of-the-box systems Deploy your models to production and maintain their performance Implement a suite of NLP algorithms using Python and PyTorch…”

“…Skill Level Intermediate Advanced Learn How To Recognize which type of transformer-based model is best for a given task Understand how transformers process text and make predictions Fine-tune a transformer-based model Create pipelines using fine-tuned models Deploy fine-tuned models and use them in production Who Should Take This Course Intermediate/advanced machine learning engineers with experience with ML, neural networks, and NLP Those interested in state-of-the art NLP architecture Those interested in productionizing NLP models Those comfortable using libraries like Tensorflow or PyTorch Those comfortable with linear algebra and vector/matrix operations Course Requirements Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels) Comfortable using the Pandas library and either Tensorflow or PyTorch Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent About Pearson Video Training: Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. …”

“…This book not only shows you how, it teaches you to understand and enhance the apps they use. - Ranjit Sahai, RAM Consulting An insightful tour of geometry using the powerful tool SymPy. You can get behind the scenes and apply these techniques to CAD and much more. - James J. …”

“…With this deeply practical book, you'll learn how to: Design an approach for solving ML and AI problems using simulations Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization (PPO) and soft actor-critic (SAO) Train ML models locally, concurrently, and in the cloud Use PyTorch, TensorFlow, the Unity ML-Agents and Perception Toolkits to enable ML tools to work with industry-standard game development tools…”

“…In the second project, you'll implement an ETL pipeline using PySpark to dump the data in MongoDB. By the end of this course, you will be able to implement any project from scratch that requires MongoDB knowledge…”

“…This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace…”

“…With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. …”

“…You’ll learn how to: Crawl and clean then explore and visualize textual data in different formats Preprocess and vectorize text for machine learning Apply methods for classification, topic analysis, summarization, and knowledge extraction Use semantic word embeddings and deep learning approaches for complex problems Work with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch…”

“…El manual incluye un caso global donde el lector podrá analizar el proceso integral de consolidación (homogeneizaciones, eliminaciones, hojas de trabajo y formulación de balance, cuenta de PyG y memoria)…”

“…Learn what deep learning can do in the enterprise Understand the general process of building and training neural networks in-house for deep learning projects Contrast building your own solution with using and deploying pre-built models Design deep learning models in the cloud with IBM Watson Studio and popular frameworks such as TensorFlow, Caffe, PyTorch and Keras…”

“…You will: Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis…”

“…This report examines: Little-known standard library modules: collections , contextlib , concurrent.futures , logging , and sched Flit for simplifying the process of submitting a Python package to the Python Package Index (PyPI) Colorama and begins for making your command-line applications friendlier for users Pyqtgraph and pywebview for creating graphical user interfaces (GUIs) Watchdog , psutil , and ptpython for working closely with the operating system Hug for exposing APIs for other users' programs to consume Arrow and parsedatetime for working with dates and times Third-party general-purpose libraries: Boltons , Cython , and the awesome-python curated list…”

“…You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects…”

“…There's no heavy theory, and you'll learn how to offload most equations to the SymPy computer algebra system…”

“…Knowledge of how to use PyCharm for debugging is also a plus, although we cover using the debugger in the course!…”

“…Before proceeding with the course, you will need basic knowledge of Spark programming in Python - PySpark…”

“…You’ll explore: How machine learning is transforming healthcare, finance, transportation, computer technology, energy, and science Use cases including self-driving cars, software development, genomics, blockchains, algorithmic trading, particle physics, and data center energy management Open source datasets and proprietary data sources for organizations that don’t generate their own unique data A typical data science life cycle, from data collection to production and scale Examples of commercial off-the-shelf (COTS) and open source machine-learning solutions—and the pros and cons of each Open source deep learning frameworks such as TensorFlow, MXnet, and PyTorch AI as a Service providers including AWS, Google Cloud Platform, Azure, and IBM Cloud Disruptive technologies that are just beginning to emerge…”

“…Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches…”