TensorFlow & Keras Deep Learning

It is built for engineering and data science students, freshers who have done a first machine learning course, and analysts who have used scikit-learn and now want to train neural networks. You should know Python and basic machine learning ideas such as a train/test split and overfitting; the course does not teach Python or classical machine learning. If those ideas are new, Machine Learning: Foundations covers them first. No neural network experience is assumed: the early lessons start from what a single neuron computes.

All code uses TensorFlow 2.x with Keras 3: keras.Sequential, the functional API, model.compile, fit and evaluate, tf.data pipelines and callbacks, with models saved in the .keras format. Older TensorFlow 1 code built on sessions is not taught. Installation, GPU setup, PyTorch and LLM fine-tuning are outside the course.

Every lesson is a few short reading cards built around a few lines of Keras, such as a model.summary() table, a loss curve or a Conv2D layer, with a diagram where one helps and audio narration you can listen to instead of reading. A short multiple-choice quiz follows, with an explanation for every answer. The questions ask what you would see if you ran the code: the output shape of a layer, how many parameters it has, which loss fits integer labels, or what a gap between training and validation loss means. Those are the checks you make when a real model trains badly. The final section compares look-alike choices, such as sigmoid and softmax, dropout and L2, or feature extraction and fine-tuning, and then works through scenarios such as serving a text classifier, running TFLite inference on a phone and debugging a loss that explodes.

Models that train badly often trace back to one of a few causes, and each has its own lesson:

• Pairing one-hot labels with a sparse loss, or integer labels with a categorical one
• Forgetting the batch dimension when declaring input shapes
• Misunderstanding how dropout and batch normalization behave at inference time
• Skipping the input preprocessing a pretrained model expects
• Fine-tuning a pretrained base with a high learning rate and wiping out what it learned
• Exploding gradients that turn the loss into nonsense, and how gradient clipping helps
• Judging a classifier on imbalanced data by accuracy alone
• Data leakage between training and validation sets

Udemy and Coursera mostly teach TensorFlow through video lectures, where an instructor builds a model in a notebook on screen. Kaggle Learn uses short interactive notebooks in which you write and run the code yourself. This course does a different job: short narrated lessons followed by questions that make you predict shapes and parameter counts, pick a loss or optimizer, and read learning curves before you train anything. Video suits watching a full project once, and notebooks suit typing code. This format builds the judgement to design a network and diagnose why it is not learning, so pair it with a notebook of your own where you run each example.

Keep a notebook with TensorFlow open beside the course and run every lesson's example. Call model.summary() often and check that each output shape matches what you expected before you train. Plot training and validation loss for every model, because the curves tell you whether to add capacity or add regularization. Change one setting at a time, such as the learning rate, the dropout rate or the batch size, and note what moves. Answer each quiz question before reading its explanation, since recalling the answer yourself is what makes it stick. By the end, build one image classifier with transfer learning and one small text classifier on data you care about. If you later need the other major framework, Deep Learning with PyTorch covers the same ideas there.