Deep Learning by TensorFlow 2.0 Basic to Advance with Python

Learn the basics of TensorFlow 2.0 with Python by defining constants and performing simple operations like addition and multiplication.

Master tensor segmentation code by building segments, analyzing their element groups, and computing min, max, and indices with conditional logic in Python and TensorFlow.

Explore regression with tf.keras model layers in TensorFlow 2.0, selecting activation functions, loss, and optimizers. Build and train a regression model using proper data shaping, sequencing, and layer configuration.

Learn to use premade estimators for classification in TensorFlow 2.0, prepare inputs, generate predictions, and interpret accuracy metrics.

Explore how to teach machines to recognize elephants using CNN basics, image dimensions, padding, and training examples.

Discover essential deep-learning terms like cnn, stride, padding, and pooling; learn how convolutional layers shape input dimensions and how max and average pooling modify features.

explains the mnist handwritten digit dataset for classification and demonstrates a cnn architecture with convolution, six filters, pooling, and flattening to predict digits.

Develop and train a convolutional neural network for image classification, focusing on MNIST data and evaluating a compact CNN model for accurate classification.

Tackle classwork on deep learning concepts, including crosswalk features, data classification with CNN and capsule networks, and sequence feature limitations in a practical TensorFlow 2.0 Python workflow.

Learn to set up an image data generator for cnn training, loading images from folders, processing in batches, applying rotation and flipping.

Explore emotion recognition with CNNs, classifying emotions into anger, disgust, happy, sad, surprise, and neutral, and learn data preparation, 48x48 input handling, model building, and evaluation.

Explore recurrent neural networks and their memory blocks for processing sequences, with applications in sentiment classification, speech cognition, and machine translation, and understand data needs and sequence modeling challenges.

Explain the vanishing and exploding gradients in backpropagation, how activation functions and initialization influence learning, and outline fixes for stability and learning efficiency.

Explore univariate time series with an LSTM model, including data reading, differencing for stationarity, normalization, reshaping into sequences, and one-step forecast.

Transfer learning is defined and explored, showing how a model trained on existing data can boost performance on a smaller dataset by fine-tuning instead of starting from scratch.

Explore basic models and transfer concepts for image-based predictions, including loading sample images and interpreting maximum-probability outputs. Learn to customize simple models for diverse images and predictions.

Identify overfitting and apply dropout, regularization, and data expansion to improve generalization. Use dropout around 50-70% in hidden layers and 10-30% at the input layer, while reducing model complexity.

Avoid overfitting by applying regularization penalties such as L2 and L1 to your neural models. Learn how to evaluate predictions, tune optimization techniques, and balance model complexity with regularization strategies.

Explore strategies to prevent overfitting with dropout, batch normalization, and early stopping, and learn how to monitor accuracy and stop training when improvements plateau.

Explore how generative adversarial networks train a generator and discriminator using cross-entropy losses, iterative gradient updates, and real versus fake image evaluation in TensorFlow 2.0.

Explore hyperparameter tuning for neural networks using Keras Tuner, defining search spaces and optimization strategies to find optimal activation functions, dropout, and other parameters for better model performance.

Explore deep learning foundations with TensorFlow 2.0, from basics to advanced concepts, including CNNs, regression, and classification, using open-source tools and practical training with real-world examples.

Explore why deep learning emerges from massive data and affordable infrastructure, and how brain-inspired neural networks power image and speech recognition and other advanced AI tasks.

Define how TensorFlow, a Google-developed open source library, uses flexible building blocks and computational graphs to enable custom calculations, graph execution, and data visualization in Python.

Explore reduction operations in tensors, computing max, min, and average across rows, columns, or the whole matrix, and learn explicit type casting to floats for input data.

Learn how TensorFlow sessions manage variables and constants, perform simple matrix operations, and debug code. Explore converting Python data to tensors and printing values to trace execution during development.

Explore regression and classification as foundational machine learning techniques, understand why they matter, and preview measurement of model capability in deep learning with TensorFlow 2.0.

Launch tensorboard to visualize model data, inspect graphs and activation histograms, and export insights from training runs, keeping folders clean for reliable visualization.

Implement multiclass classification in TensorFlow 2.0 by building a simple neural model with one-hot encoding, softmax, cross-entropy loss, and accuracy evaluation with a confusion matrix.

Build a multi-class classifier with TensorFlow 2.0, using a dense network, softmax outputs, and a loss suited for categorical outcomes; train, evaluate with accuracy, and make predictions.

Explore how convolution in CNNs performs feature extraction on images, preserves spatial arrangement, and reduces parameters by flattening image data into a single dimension for efficient learning.

Explore convolutional neural networks applied to the MNIST handwritten digits dataset, detailing a classic CNN architecture, image input formats, filters and pooling, and TensorFlow-based training for digit classification.

Leverage core TensorFlow to build an MNIST classifier by flattening images to 784 features, applying softmax with cross-entropy loss, and assessing performance via accuracy and a confusion matrix.

Learn to build a tf.keras model for MNIST by flattening 28x28 images, applying softmax, normalizing data, and optimizing with suitable loss and optimizers to produce label probabilities.

Learn tf.keras model layers for mnist classification using softmax after flattening the input. Analyze loss, accuracy, and prediction probabilities to select the max probability label.

Develop a cnn for mnist using tf layers without flattening the data, building conv layers, max pooling, and a softmax classifier with loss and accuracy evaluation.

Explore building a Keras CNN for image classification, detailing data preparation, 3x3 convolutions, pooling, flattening, a 128-unit dense layer, and a 10-class softmax classifier trained with cross-entropy loss.

Learn to evaluate a Keras CNN model, compare architectures, reach about 92–93 percent accuracy, and interpret predictions by decoding one-hot outputs via the confusion matrix.

Develop emotion recognition with CNN classification using Keras, processing 48 by 48 black and white face images, reshaping inputs, and predicting expressions like happy while comparing CNN and capsule networks.