Arama Sonuçları - do NOT adjust (your OR you) set

Trade Unions on YouTube Online Revitalization in Sweden / Yazar: Jansson, Jenny, Uba, Katrin

İçindekiler: “…Introduction -- 2. Audiences: Who Do Unions Target? -- 3. Messages: Political Action - Agenda-Setting, Elections and Protest -- 4. …”
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Reading / Yazar: Davis, Philip (Philip Maurice), Magee, Fiona

İçindekiler: “…-- Section 1: What Is Engaged Reading? -- Section 2: How to Do This Kind of Engaged Reading? -- Things To Do -- On My Own -- Things to do on my own (1): Try the reading app -- Things to do on my own (2): Making notes or writing a reading diary -- Things to Do -- With Others -- Things to Do -- for Others -- Things to See and Hear -- Notes -- 5: What Can Professionals Do to Help? …”
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Adolescent experiences and adult work outcomes

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From Bench to Boardroom The R&D Leader's Guide / Yazar: Spiro, Clifford L.

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Identifying leaders for urban charter, autonomous and independent schools above and beyond the standards /

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Accessible instructional design

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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…-- 3.3 Keras and TensorFlow: A brief history -- 3.4 Setting up a deep learning workspace -- 3.4.1 Jupyter notebooks: The preferred way to run deep learning experiments -- 3.4.2 Using Colaboratory -- 3.5 First steps with TensorFlow -- 3.5.1 Constant tensors and variables -- 3.5.2 Tensor operations: Doing math in TensorFlow -- 3.5.3 A second look at the GradientTape API -- 3.5.4 An end-to-end example: A linear classifier in pure TensorFlow -- 3.6 Anatomy of a neural network: Understanding core Keras APIs -- 3.6.1 Layers: The building blocks of deep learning -- 3.6.2 From layers to models -- 3.6.3 The "compile" step: Configuring the learning process -- 3.6.4 Picking a loss function -- 3.6.5 Understanding the fit() method -- 3.6.6 Monitoring loss and metrics on validation data -- 3.6.7 Inference: Using a model after training -- Summary -- 4 Getting started with neural networks: Classification and regression -- 4.1 Classifying movie reviews: A binary classification example -- 4.1.1 The IMDB dataset -- 4.1.2 Preparing the data -- 4.1.3 Building your model -- 4.1.4 Validating your approach.…”
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Generative AI with LangChain : Build Large Language Model (LLM) Apps with Python, ChatGPT, and Other LLMs. Yazar: Auffarth, Ben

İçindekiler: “…-- Other LLMs -- Major players -- How do GPT models work? -- Pre-training -- Tokenization -- Scaling -- Conditioning -- How to try out these models -- What are text-to-image models? …”
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Transforming virtual world learning

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Disputes in everyday life social and moral orders of children and young people /

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Gender and race matter global perspectives on being a woman.

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Video research in disciplinary literacies

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Innovation in Language Teaching and Learning The Case of Japan /

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