ChatGPT for Deep Learning with Python Keras and Tensorflow

Explore how ChatGPT and GPT models, developed by OpenAI, generate text through deep learning and pattern recognition, using prompts as sequences of characters without true understanding.

Explore how ChatGPT delivers direct, conversational answers and how search engines return keyword-based links, and learn about integrating AI and search in one query.

Compare ChatGPT’s neural networks and data-driven learning with human intuition, noting non-deterministic outputs shaped by temperature and the AI's scale, speed, and consistency in processing vast data.

Create a ChatGPT account and log in using Google, Microsoft, Apple ID, or email with two factor authentication. Explore the chat interface, manage conversations, and learn about GPT-3.5 by default.

July 2024 updates explain ChatGPT model options, including GPT-4 variants, plus free versus plus plans, rate limits, and features like data analysis, file uploads, browsing, Dall-E, and custom GPTs.

Explore how ChatGPT relates to GPT models, compare GPT-3.5 and GPT-4, and review plus, plugins, web browsing, API, and fine-tuning options.

Navigate the OpenAI website to explore ChatGPT, DALL-E, and API usage, while learning about safety, data privacy, tokens, and pricing for model use.

Explore tokens as the building blocks of language models, learn how text is converted into token sequences and numbers, and understand token limits across GPT models and tokenization details.

Master prompting techniques with explicit instruction to extract precise, helpful responses from ChatGPT. Follow clear steps—goal, background, scope—with a Titanic data science project example to illustrate practice.

Master iterative refinement to improve ChatGPT responses through feedback and clarifications, focusing on data pre-processing, explanatory data analysis, and providing practical Python code.

Explore prompting techniques that tailor audience tone, detail level, and response format. Compare supervised and unsupervised learning with Titanic and spam-detection examples.

Install Python and the data science ecosystem with Anaconda to manage dependencies and work with Jupyter notebooks, PyCharm, or Spyder across Windows, Mac, and Linux.

Open the Anaconda Navigator and launch Jupyter Notebook to write your Python lines. Manage the base environment, check packages like numpy and pandas, and run cells with shift+enter or alt+enter.

Master Jupyter notebooks' core features—edit and command modes, cells, and keyboard shortcuts. Learn markdown, headers, images, and memory management with kernel restart and common error fixes.

Create a dedicated deep learning environment in Anaconda Navigator using Python 3.10, install TensorFlow and Keras, add scikit-learn, pandas, seaborn, matplotlib, and statsmodels, and set up Jupyter Notebook.

Compare deep learning and neural networks—FFNNs, CNNs, RNNs, LSTMs—with traditional models like linear regression and SVMs for image recognition and natural language processing, and weigh training versus GPT prompts.

Explore common neural network subtypes, including CNNs, RNNs, LSTMs, autoencoders, and GANs, and match each architecture to its data type, such as image recognition, with real-world applications.

Explore how a simple feedforward neural network maps inputs to outputs via one hidden layer with two neurons, weights, biases, and sigmoid activation for binary classification.

Compare feedforward networks with convolutional and recurrent networks, highlighting CNNs' preservation of spatial structure for images and RNNs' memory for sequences such as language and time series.

Compare pre-trained GPT models with custom neural networks for regression, classification, image recognition, time series, and NLP, and learn when to fine-tune versus train from scratch using Keras and TensorFlow.

Load a census CSV into Python and pandas, inspect data structure and features with ChatGPT, brainstorm analyses from machine learning to A/B testing, and plan data cleaning and feature engineering.

Explore how GPT-4 upgrades alter prompting strategies, delivering faster, higher quality responses with longer token limits; only GPT-4 row executes code using Python std library, NumPy, pandas, and matplotlib.

Use ChatGPT as your personal assistant to complete project one data inspection: load census data, inspect structure, clean data, create features, save cleaned dataset, and brainstorm ML and DL ideas.

Learn to prepare a dataset for gpt-3.5 and gpt-4o mini by selecting representative csv rows mindful of token limits.

Inspect the census data sample with pandas to understand the dataset structure, viewing each row as an observation and each column as a property for machine learning and predictive analysis.

Brainstorm census data uses with GPT-3.5 and GPT-4o mini, covering explanatory data analysis and classification for income levels, plus data cleaning and feature engineering.

Use GPT-3.5 to guide data cleaning with pandas, identifying missing values, outliers, and inconsistencies, and transforming binary variables for a census income classification model.

Engineer new features to improve model performance, including education level, marital status, full-time indicator, and capital net gain; explore prompts and missing-feature brainstorming for income prediction.

learn to save the fully cleaned data frame to a local csv file using pandas to_csv, define an output path, drop the index, and verify with read_csv.

Explore loading datasets with GPT-4 / GPT-4o, upload census CSV files, and use the built-in Python data analysis stack to read, confirm access, and proceed with analysis.

Inspect census data with Pandas, review structure and data types, and brainstorm deep learning applications such as income prediction using age, education, occupation, and hours per week.

Clean and normalize dataset using GPT-4 to fix missing values, outliers, and inconsistent data, employing pandas code to replace placeholders with NaN, apply 1.5×IQR outlier capping, and lowercase categorical variables.

Explore the limitations of GPT-4 for code execution, including loss of execution state after breaks, and learn practical troubleshooting steps such as restarting conversations, re-uploading data, and re-running the code.

Derive new features from existing data to improve predictive model performance, guided by GPT-4, including a college degree indicator, age categories, capital gains or losses, and work hours categories.

Learn how to download and save the cleaned dataset from GPT-4 as a csv file to your local device, including new columns and careful filename handling.

Compare GPT-3.5 and GPT-4 features for data analysis with Python, highlighting file handling, code execution, and critical human oversight; troubleshoot common errors and connectivity issues.

Explore explanatory data analysis with ChatGPT and the Python data science stack using a cleaned census dataset. Analyze univariate, bivariate, and multivariate features and discuss implications for deep learning projects.

Perform an end-to-end exploratory data analysis on census data with Python and ChatGPT, focusing on income, univariate and bivariate analyses, and deriving features for ML/DL projects in a notebook.

Load the census dataset in pandas, inspect 19 columns and features like has college degree and income greater than 50,000. Begin EDA with initial inspection and basic statistics.

Explore behind the scenes of preparing a clean dataset: handle missing values, normalize data, cap outliers with interquartile ranges, create new features, and save the cleaned csv for analysis.

Brainstorm the goals and objectives of an explanatory data analysis on the census data set. Emphasize understanding distributions, relationships, data cleaning, preparing for predictive modeling via feature engineering and visualizations.

Explore univariate data analysis by examining the distribution of each feature separately with histograms and count plots, using descriptive statistics and kernel density estimates.

Explore multivariate data analysis by building a correlation matrix and visualizing a seaborn heat map; discover how income relates to age and education, and remember correlation does not imply causation.

Explore how age, education, hours worked, and marital status influence income through bivariate and multivariate analysis of numerical and categorical features, including capital gains and college degrees.

Explore multivariate data analysis to uncover interactions among age, education, work class, and income. Use methods like multiple logistic regression, decision trees and random forests, PCA, clustering, and feature engineering.

Explore running Python code for exploratory data analysis in Jupyter notebooks, generating univariate and multivariate plots (including correlation matrices and heatmaps) with pandas, seaborn, and matplotlib, while troubleshooting version issues.

Explore creating, fitting, and optimizing a feed forward neural network for binary census classification with Keras and TensorFlow, including feature selection, preprocessing, and thresholds, plus GPT-3.5/4 prompting insights.

Define, fit, and optimize a deep learning classifier with Keras and TensorFlow to predict high income individuals from census data. Build a scikit-learn pipeline for preprocessing, integration, and hyperparameter tuning.

Load the census data from the course materials and inspect it in a Jupyter notebook with pandas, verifying nearly 49,000 observations and 19 columns via df.info.

Brainstorm a classification project with a feedforward neural network on census data to predict income, detailing data preprocessing, encoding, scaling, model design, evaluation, and scikit-learn pipelines for hyperparameter tuning.

Explore data pre-processing and feature engineering for deep learning with keras and tensorflow, covering missing values, feature scaling, one-hot encoding, binary representations, and handling imbalanced data.

Explore feature selection and preprocessing, compare hours per week with work hours category, favor education num over education, and address census data imbalance with class weighting in keras and tensorflow.

simplify the native country feature, remove redundant columns, and export the final census data set for a deep learning model, illustrating data pre-processing and feature selection steps.

Construct a Python data preprocessing pipeline to impute missing values, encode categorical features with dummies, and scale numerical features, then split data for training a TensorFlow Keras model.

Build and fit a baseline feedforward neural network in a census data preprocessing pipeline with 48 features, two hidden layers (64 and 32), and binary income classification (>50k).

Evaluate baseline model on the test set using accuracy and classification report, noting training loss of 0.29 and ~85% test accuracy on income data, with plans to address class imbalance.

Explore model optimization theory for a network classification task, addressing class imbalance with weights, tuning learning rate, layers, activation, and dropout, and applying cross-validation with grid, random, and bayesian optimization.

This lecture guides implementing hyperparameter tuning with random search and cross-validation for a Keras and TensorFlow model, adding dropout, and applying class weights to address imbalance.

Compare model performance with and without class weighting, noting higher recall but lower precision for the minority class, and discuss hyperparameter tuning and architecture changes to optimize accuracy and F1.

Modify the model by adding or removing hidden layers, tuning neurons and dropout, and re-run optimization to compare architectures, while addressing class imbalance with weighting and adjusting the decision rule.

Adjust decision thresholds to balance precision and recall in imbalanced data, showing how higher thresholds boost precision while lowering recall, with code and confusion-matrix diagnostics.

Explore a full census dataset deep learning project using GPT-4 prompts, focusing on data pre-processing, one-hot encoding, class weights, and a two-layer Keras neural network for training and evaluation.

Compute precision, accuracy, recall, F1, and ROC curve on the test set, while addressing class imbalance and guiding hyperparameter tuning with cross-validation, class weights, and dropout.

We build, fit, and evaluate an fn neural network with two hidden layers, tune hyperparameters with randomized search cv, incorporate class weights and threshold adjustment, and assess feature importance.

Apply scikit-learn permutation importance to a trained pipeline, visualize results with a box plot, and interpret top features such as capital changes, education num, and occupation.

Explore image recognition with convolutional neural networks using Keras and TensorFlow, starting from loading CIFAR-10, preprocessing, training a CNN with data augmentation, validation, and checkpointing, plus batch normalization and dropout.

Develop and evaluate a showcase CNN for CIFAR-10 image recognition using Keras and TensorFlow, loading and preprocessing data, training a baseline, and improving with augmentation, validation, and checkpointing.

Download the CIFAR-10 Python version, a dataset of 60,000 color images across ten classes, with 50,000 training and 10,000 test images, then unpack and load the pickled batches in Python.

Load and inspect the CIFAR ten dataset in python using a jupyter notebook, unpickle batch files, reshape to 32x32x3, visualize images, and map labels to names for practical deep learning workflows.

Create a Python function with parameter x to display first x images from data batch one in a Jupyter notebook, handling unpickle and label names, reshaping and transposing for matplotlib.

Load and merge five training batches into a single data set for display and neural network training with Keras and TensorFlow, and save as a numpy compressed npz file.

Normalize image pixel values to 0-1 and convert labels to one-hot encoding for ten classes, preparing loaded data and labels for training a neural network, while preserving image appearance.

Explore a structured approach to training, evaluating, and optimizing a CIFAR-10 image classifier with TensorFlow and Keras, starting from a baseline CNN and using data augmentation, regularization, and early stopping.

Build and train a baseline CNN on cifar ten dataset with TensorFlow, featuring three conv layers, max pooling, flatten, dense layers, softmax for categories, and early stopping, 20% validation split.

Analyze the baseline model’s training and test outputs to diagnose overfitting and assess generalization. Use data augmentation, architecture tweaks, regularization, and hyperparameter tuning to improve performance.

Apply data augmentation with rotation, shift, zoom, and flip to improve model generalization. Experiment with architecture changes—batch normalization, dropout, more layers—plus Keras image data generator and hyperparameter tuning.

Learn how to save the best model during training with Keras model checkpointing, using a file path and a callback alongside early stopping to maximize validation accuracy.

This lecture covers advanced data augmentation and fine tuning for cifar-10, iterating on rotation, shifts, and preprocessing to boost accuracy toward 76%, with notes on limitations for cats and dogs.

Increase model complexity with more convolutional layers, higher filters, batch normalization, and a larger dense layer, while monitoring for overfitting on CIFAR ten dataset and planning dropout later.

Apply dropout to prevent overfitting in the model, tune 0.2–0.5 rates after activation or between dense layers, achieving about 88–90% validation accuracy.

Enrich time series prediction with recurrent neural networks, using an LSTM baseline to forecast energy consumption, and load data, engineer features, validate, and interpret results in a Keras TensorFlow project.

Apply an lstm or rnn to forecast hourly household energy consumption using energy.csv, building and evaluating a stepwise time-series model with lag features in a Keras and TensorFlow Jupyter notebook.

Conduct a brief explanatory data analysis of household energy consumption to visualize time-based trends, including monthly and weekly patterns and cross-year seasonal variations (2006–2010).

Outline a deep learning plan to predict the upcoming hour's energy consumption using time series data, emphasizing preprocessing, time features, lagged values, rolling averages, and an LSTM architecture.

Explore covariance stationarity in time series for deep learning, learn when LSTMs benefit from stationary data, and apply the augmented Dickey-Fuller test to electricity data to assess stationarity.

Learn to create temporal features for energy prediction with LSTM models using Python and TensorFlow, including hour, day, month, season, weekend, and sine-cosine encoding.

Create a baseline lstm model with 24 hourly lags in Keras and TensorFlow to predict the upcoming hour's total energy consumption using only past consumption.

Predict training and test set outcomes, then inverse transform the scaled predictions. Compute root mean squared error and r squared, and visualize actual versus predicted values for the LSTM model.

Explore how to select the optimal look-back period (lags) for energy consumption forecasting, comparing 1, 24, and 24x7 lags and highlighting the sweet spot at 24 for accuracy and efficiency.

Add features to the LSTM model by incorporating a second feature with the same 24 lags, adjust data preparation, and evaluate with root mean squared error and R-squared.

Explore lagged features for energy prediction with an lstm model, using eight features and 24 lags to achieve 60% explained variance; plan to add temporal features in the next lecture.

Add temporal features to an LSTM model for energy consumption forecasting using one-hot encoded season and weekend indicators, combining lagged features with known future values to improve predictions.

Increase the LSTM architecture by stacking layers, adding dropout, bidirectional LSTMs, and regularization, while tuning neurons, learning rate, and optimizer with early stopping and monitoring training and validation loss.

Implement model checkpointing for the validation loss and early stopping with patience four, use a 20% validation split, and train a two-layer lstm (150 and 100 neurons) with two dropouts.

Practice building an LSTM model with Keras and TensorFlow to predict hourly energy consumption, analyze its performance, and iteratively improve via feature engineering, rolling averages, and hyperparameter tuning.

Explore using chatgpt to generate pandas code and review results for clearer understanding. Learn pandas basics and pandas advanced through jupyter notebooks, exercises, and course materials with datasets.

Explore tabular data and pandas data frames, where rows are observations and columns are features with labeled indices and headers. Learn how one data type per column enables dataframe functionality.

Set up a new Jupyter notebook, import pandas, and load Titanic.csv into a data frame named Titanic from the same folder, revealing 891 rows and nine columns.

Load a CSV from another location in a Jupyter notebook with pandas read_csv using the full file path, and adapt for Windows, macOS, or Linux with the R prefix.

Learn how pandas displays data frames in notebooks versus print output, adjust max rows and min rows, and use head and tail to inspect the first and last rows.

Inspect the dataset by examining the first rows, column headers, and data types with pandas info and describe to plan cleaning and handling missing values.

Explore how Python built-ins and pandas dataframes work, using Titanic to show attributes like shape, size, index, and columns, and methods such as head, min (numeric_only), and mean with chaining.

Boost pandas coding efficiency with tab completion and tooltips, exploring auto completion, parameter hints, and practical examples like reading data and sorting by age.

Learn to select one or more columns in pandas using labels or a list, and see when the result is a series or a data frame.

Explore selecting a single column in a pandas data frame using dot notation or square brackets, compare results with the equals method, and note spaces in column labels.

Learn how to select rows, columns, and elements by position using zero-based and negative indexing in a two-dimensional data frame, with practical examples from rows and columns.

Explore position based indexing with iloc in pandas using the Summer Olympic Games dataset, selecting rows by index positions, slicing, and handling negative indexing.

Master position-based indexing with iloc to select specific cells, rows, and columns in a summer dataset, including single elements, ranges, and full columns.

Explore label-based indexing with the loc operator to select rows by label, compare it to position-based indexing, and retrieve single or multiple rows such as Dimitrios Drivas and Michael Phelps.

Explore label-based indexing with loc in pandas, including selecting rows and columns, handling duplicates, and understanding boundary rules and the pitfalls of non-unique indexes.

Explore pandas series as a one-dimensional labeled array by selecting an age column from the Titanic data frame, compare bracket and attribute access, and convert to a frame with to_frame.

Analyze numerical data in a pandas series with describe, unique, nunique, and value_counts to reveal counts, missing values, and distribution. Explore dropna and skipna effects, relative frequencies, normalization, and binning.

Analyze a pandas series with non-numerical values using the Summer Olympic medal data set. Use unique(), nunique(), and value_counts() to reveal frequencies, identify unique athletes, and compare describe results.

Learn to work with the pandas index object, inspect row and column indices, slice indices, customize with read_csv, set an athlete column as row index, and use get_loc for positions.

Learn to filter data frames with boolean masks in pandas, selecting rows by column conditions (such as male Titanic passengers) and filtering numeric columns using the log notation.

Demonstrates filtering a data frame by multiple conditions using boolean masks and the and operator to create a subset, such as adult male Titanic passengers older than 14.

learn to sort pandas data frames and series with sort_values and sort_index, including single and multi-column sorting by age, pclass, and sex, with ascending or descending order and ignore index.

Plot Titanic data using pandas' plot with matplotlib, explore subplots, fixed size, and share settings, and visualize the age distribution as a line plot.

Create histograms of Titanic age data using pandas, matplotlib, and seaborn to visualize frequency distributions, use value counts, and explore bins from 0 to 80 with a ten-bin default.

Create two dimensional scatter plots to explore relationships between numerical features such as age and fare in the Titanic dataset, using color and markers to show survival and class.

Learn how pandas group by splits a data frame by the sex key into female and male groups, creating a group by object and enabling iteration and further operations.

Learn to split a data frame by multiple keys using group by, exploring country and gender splits in the Summer Olympics data, and understand how to handle many groups efficiently.

explore split-apply-combine with group by in pandas, applying mean and sum to the titanic data to compare surviving rates, ages, fares, and other columns across female and male groups.