“…Let's learn how to write Apache Spark Streaming programs with PySpark Streaming to process big data sources today!…”

Tabla de Contenidos: “…-- Conda environment management -- Managing Python -- Package management -- Setting up a database -- Installing MySQL -- MySQL connectors -- Creating a database -- Summary -- Chapter 2: Diving into NumPY -- NumPy arrays -- Special numeric values -- Creating NumPy arrays -- Creating ndarray -- Summary -- Chapter 3: Operations on NumPy Arrays -- Selecting elements explicitly -- Slicing arrays with colons -- Advanced indexing -- Expanding arrays -- Arithmetic and linear algebra with arrays -- Arithmetic with two equal-shaped arrays -- Broadcasting -- Linear algebra -- Employing array methods and functions -- Array methods -- Vectorization with ufuncs -- Custom ufuncs -- Summary -- Chapter 4: pandas are Fun! …”

“…Advanced analytics with PySpark. Polish…”

“…Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. …”

“…PyTorch for deep learning and computer vision…”

Tabla de Contenidos: “…Intro -- Inhalt -- Einführung -- Teil I: PyTorch und neuronale Netze -- Kapitel 1: Grundlagen von PyTorch -- Google Colab -- PyTorch-Tensoren -- Automatische Gradienten mit PyTorch -- Berechnungsgraphen -- Lernziele -- Kapitel 2: Erstes neuronales Netz mit PyTorch -- Das MNIST-Bilddatensatz -- Die MNIST-Daten abrufen -- Ein Blick auf die Daten -- Ein einfaches neuronales Netz -- Das Training visualisieren -- Die Klasse für den MNIST-Datensatz -- Unsere Klassifizierer trainieren -- Das neuronale Netz abfragen -- Die Performance des Klassifizierers einfach ermitteln -- Kapitel 3: Verfeinerungen -- Verlustfunktion -- Aktivierungsfunktion -- Optimierungsmethode -- Normalisierung -- Kombinierte Verfeinerungen -- Lernziele -- Kapitel 4: Grundlagen von CUDA -- NumPy vs. …”

“…Build better PyTorch models with TensorBoard visualization About This Video Learn everything you need to know to start using TensorBoard in PyTorch with practical examples in Machine Learning, Image Classification, and Natural Language Processing (NLP) Launch TensorBoard from any developer environment, including Jupyter notebooks and Google Colab Visualize and optimize your PyTorch models using techniques such as model graphs, training curves, image data, text embeddings, and many more In Detail TensorBoard is a visualization library for TensorFlow that plots training runs, tensors, and graphs. …”

“…This video will serve as an introduction to PyTorch, a dynamic, deep learning framework in Python. …”

“…This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. …”

“…Deep learning with PyTorch. Chinese…”

“…Deep learning is revolutionizing the field of artificial intelligence, enabling machines to learn and make decisions like humans. PyTorch, a powerful and flexible deep learning framework, has emerged as the preferred tool for building and training neural networks. …”

“…Explanation of distributed gradient descent with an algorithm Horovod using multivariate linear regression in PyTorch and Python…”

Tabla de Contenidos: “…Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Section 1: Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the ANN building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction -- Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Learning rate of 0.01 -- Learning rate of 0.1 -- Learning rate of 1 -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors -- Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- Using state_dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars -- Creating a structured array for colored images -- Why leverage neural networks for image analysis?…”

“…Elegant SciPy. Chinese…”

Acceso restringido con credenciales UPSA