Predict the stock market with data and model building!

Learn the basics of TensorFlow in Python, set up a new project, explore computational graphs, train a simple linear regression model, and prepare for future machine learning tutorials.

Explore TensorFlow, a massive library for machine learning with high-level APIs and built-in datasets. Learn to build and train computation graphs using tensors across platforms like Python, mobile, and web.

Explore constant nodes and operation nodes to build a computational graph for a linear regression model, including creating, running sessions, and using placeholders and variable nodes.

Learn how placeholder nodes hold input values until a session runs, providing input for models like linear regression, and why every placeholder must receive a value before execution.

Discover how variable nodes differ from constant nodes, including initial value assignment, the need for initialization before evaluation via a global variables initializer, and the rich functions available for variables.

Explore how to build a linear regression model from scratch using computational graphs, placeholders, variables, and loss minimization to predict stock data.

Build and train a linear regression model in TensorFlow using placeholders, variables, and a gradient-descent optimizer to minimize loss.

Develop a simple stock market prediction model using daily volume to forecast next-day price movement for day trading, with a Python project and data from investing dot com.

Predict whether the next morning's stock price rises or falls using end-of-day volume, via a simple model trained on gold exchange stock data.

Import stock data from CSFB CSV sheets using pandas, clean values, and extract open prices and volumes. Create a load_stock_data function to return floating arrays for training and testing.

Train a stock prediction model by creating a TFT session, initializing variables, and feeding training and testing data through placeholders to generate price difference datasets.

Explore the foundations of machine learning, neural networks, and convolutional neural networks through a no-coding, video-led session with text notes, focusing on four steps in building ML programs.

Explore neural networks, their layers, weights, biases, and activation functions that route input to output, and how training adjusts weights to recognize patterns for image classification.

Explore convolutional neural networks, which apply convolution and max pooling to image data, reduce input size, and highlight important features for better recognition.

Explore Keras and TensorFlow libraries to build different parts of a machine learning model, train, evaluate, and predict, while installing Keras and reviewing activation functions.

Master keras syntax to design a sequential model for stock data, add convolutional, max pooling, and dense layers with dropout, compile with an optimizer, and train, evaluate, and predict.

Explore activation functions like identity, binary step, sigmoid, tanh, ReLU variants, leaky ReLU, and softmax, and see how they shape neuron outputs for CNN classification.

Learn to format input and output data for the CIFAR-10 dataset, explore the dataset and its data points, and format inputs, labels, and model output for image classification.

Explore the CIFAR-10 dataset with Python by downloading and loading 60,000 color images, 32 by 32, across 10 classes for image classification, with 50,000 training and 10,000 testing samples.

Learn how 60k train and 10k test images are stored as 32 by 32 rgb arrays with matching labels, then normalize and convert labels to categorical format for model training.

Build a sequential convolutional neural network from scratch for stock market prediction, layering convolution, max pooling, flatten, dense and dropout, with a softmax output and max-norm constraints.

Import and format the inputs, then compile the model with a sagd optimizer and cross-entropy loss. Train with a batch size of 32 for 10 epochs and evaluate accuracy.

Learn to save and load trained models with H5 files, evaluate and use model predict functions, and freeze the graph for mobile import.

Save a pre-trained TensorFlow model to a protobuf file by freezing the graph and converting variables to constants, enabling mobile app integration on Android and iOS.

Build an image classifier using Karris and Python in a seven-day boot camp. Learn neural networks, convolutional networks, data preparation, and how to build, train, and save models for deployment.